Showing papers in "IEEE Transactions on Power Systems in 2015"
TL;DR: In this paper, a stochastic battery with dissipation model was proposed to characterize the power limits and energy capacity of a collection of thermostatically controlled loads (TCLs) in terms of TCL parameters and random exogenous variables such as ambient temperature and user-specified set points.
Abstract: It is widely accepted that thermostatically controlled loads (TCLs) can be used to provide regulation reserve to the grid. We first argue that the aggregate flexibility offered by a collection of TCLs can be succinctly modeled as a stochastic battery with dissipation. We next characterize the power limits and energy capacity of this battery model in terms of TCL parameters and random exogenous variables such as ambient temperature and user-specified set-points. We then describe a direct load control architecture for regulation service provision. Here, we use a priority-stack-based control framework to select which TCLs to control at any time. The control objective is for the aggregate power deviation from baseline to track an automatic generation control signal supplied by the system operator. Simulation studies suggest the practical promise of our methods.
623 citations
TL;DR: The works that have contributed to the modeling and computational aspects of stochastic optimization (SO) based UC are reviewed to help transform research advances into real-world applications.
Abstract: Optimization models have been widely used in the power industry to aid the decision-making process of scheduling and dispatching electric power generation resources, a process known as unit commitment (UC). Since UC’s birth, there have been two major waves of revolution on UC research and real life practice. The first wave has made mixed integer programming stand out from the early solution and modeling approaches for deterministic UC, such as priority list, dynamic programming, and Lagrangian relaxation. With the high penetration of renewable energy, increasing deregulation of the electricity industry, and growing demands on system reliability, the next wave is focused on transitioning from traditional deterministic approaches to stochastic optimization for unit commitment. Since the literature has grown rapidly in the past several years, this paper is to review the works that have contributed to the modeling and computational aspects of stochastic optimization (SO) based UC. Relevant lines of future research are also discussed to help transform research advances into real-world applications.
519 citations
TL;DR: In this paper, the authors proposed a comprehensive operation and self-healing strategy for a distribution system with both dispatchable and non-dispatchable distributed generators (DGs), where a rolling-horizon optimization method is used to schedule the outputs of dispatchable DGs based on forecasts.
Abstract: This paper proposes a novel comprehensive operation and self-healing strategy for a distribution system with both dispatchable and nondispatchable distributed generators (DGs). In the normal operation mode, the control objective of the system is to minimize the operation costs and maximize the revenues. A rolling-horizon optimization method is used to schedule the outputs of dispatchable DGs based on forecasts. In the self-healing mode, the on-outage portion of the distribution system will be optimally sectionalized into networked self-supplied microgrids (MGs) so as to provide reliable power supply to the maximum loads continuously. The outputs of the dispatchable DGs will be rescheduled accordingly too. In order to take into account the uncertainties of DG outputs and load consumptions, we formulate the problems as a stochastic program. A scenario reduction method is applied to achieve a tradeoff between the accuracy of the solution and the computational burden. A modified IEEE 123-node distribution system is used as a test system. The results of case studies demonstrate the effectiveness of the proposed methodology.
498 citations
TL;DR: In this article, a linear model is proposed for the centralized dispatch for integrated energy systems considering both heat and power, with detailed modeling of the charging processes of the heat storage tanks.
Abstract: With the largest installed capacity in the world, wind power in China is experiencing a $\sim 20\%$ curtailment during operation. The large portion of the generation capacity from inflexible combined heat and power (CHP) is the major barrier for integrating this variable power source. This paper explores opportunities for increasing the flexibility of CHP units using electrical boilers and heat storage tanks for better integration of wind power. A linear model is proposed for the centralized dispatch for integrated energy systems considering both heat and power, with detailed modeling of the charging processes of the heat storage tanks. The model balances heat and power demands in multiple areas and time periods with various energy sources, including CHP, wind power, electrical boilers, and heat storage. The impact of introducing electrical boilers and heat storage systems is examined using a simple test system with characteristics similar to those of the power systems in Northern China. Our results show that both electrical boilers and heat storage tanks can improve the flexibility of CHP units: introducing electrical boilers is more effective at reducing wind curtailment, whereas heat storage tanks save more energy in the energy system as a whole, which reflect a different heating efficiency of the two solutions.
460 citations
TL;DR: In this article, a mixed-integer linear programing (MILP) formulation that couples power and gas networks taking into account the gas traveling velocity and compressibility is presented.
Abstract: The significant growth in gas-fired units worldwide has increased the grade of interdependency between power and natural gas networks. Since these units are usually required to ramp up during the peak and backup intermittent renewable generation and contingencies, the power system tends to demand more flexibility and reliability from the gas system. This paper contributes with a novel mixed-integer linear programing (MILP) formulation that couples power and gas networks taking into account the gas traveling velocity and compressibility. As a result, the model accounts for the gas adequacy needed to assure the power system reliability in the short term. The robustness of the MILP formulation allows guaranteeing global optimality within predefined tolerances. Case studies integrate the IEEE 24-bus system and Belgian high-calorific gas network for validating the formulation.
377 citations
TL;DR: In this paper, a synchronous resonance (SSR) was observed in wind farms located in North China, which consist of doubly-fed induction generators and are connected to series-compensated transmission lines.
Abstract: Subsynchronous resonance (SSR) was observed in wind farms located in North China. These wind farms prevailingly consist of doubly-fed induction generators (DFIGs) and are connected to series-compensated transmission lines. The observed resonant frequency is about 6 $\sim$ 8 Hz, which is much lower than that of the reported SSR occurred in Texas. The frequency varies during the occurrence and this phenomenon is observed for the first time. The output power is usually within a certain range, when SSR occurs. Based on the practical system, an equivalent simulation system has been established, in which wind farms are modeled as many identical low rating DFIGs. Then, the SSR event is reproduced by simulations. Analysis results indicate that SSR happens even when the equivalent transmission system compensation level seen from wind farms is only 6.67%. Eigenvalue analysis shows that this phenomenon is an electrical resonance, and could be affected considerably by wind speed, number and control of DFIGs. The number of in-service DFIGs has a nonlinear impact on the damping of SSR. An equivalent electric circuit is deduced to intuitively explain why SSR happens and how the above factors affect it. Considering its features, this phenomenon is recognized as DFIG control participated induction generator effect.
354 citations
TL;DR: In this article, an adaptive robust optimization model for multi-period economic dispatch, and methods to construct such sets to model temporal and spatial correlations of uncertainty, are presented to deal with uncertainty caused by the highly intermittent and uncertain wind power becomes a significant issue.
Abstract: The exceptional benefits of wind power as an environmentally responsible renewable energy resource have led to an increasing penetration of wind energy in today's power systems. This trend has started to reshape the paradigms of power system operations, as dealing with uncertainty caused by the highly intermittent and uncertain wind power becomes a significant issue. Motivated by this, we present a new framework using adaptive robust optimization for the economic dispatch of power systems with high level of wind penetration. In particular, we propose an adaptive robust optimization model for multi-period economic dispatch, and introduce the concept of dynamic uncertainty sets and methods to construct such sets to model temporal and spatial correlations of uncertainty. We also develop a simulation platform which combines the proposed robust economic dispatch model with statistical prediction tools in a rolling horizon framework. We have conducted extensive computational experiments on this platform using real wind data. The results are promising and demonstrate the benefits of our approach in terms of cost and reliability over existing robust optimization models as well as recent look-ahead dispatch models.
326 citations
TL;DR: In this paper, the synchronous generator emulation control (SGEC) strategy for the VSC-HVDC station is presented, which is divided into the inner control loop and the outer control loop.
Abstract: The voltage source converter (VSC) station is playing a more important role in modern power systems, but the dynamic behavior of the VSC station is quite different from that of the synchronous generator. This paper presents the synchronous generator emulation control (SGEC) strategy for the VSC-HVDC station. The SGEC strategy is divided into the inner control loop and the outer control loop. The inner controller is developed for fast current and voltage regulations. An inertia element is introduced into the frequency-power droop to determine the command reference of the frequency, and the inertia response and the primary frequency regulation are emulated. In addition, the secondary frequency regulation can be achieved by modulating the scheduled power in the SGEC strategy. The time-domain simulation results demonstrate the VSC station with the proposed control strategy can provide desired frequency support to a low-inertia grid. Therefore, the SGEC strategy provides a simple and practical solution for the VSC station to emulate the behavior of a synchronous generator.
319 citations
TL;DR: In this article, a robust optimization approach is adopted for considering forecast errors in load, variable renewable generation, and market prices, and the microgrid islanding is further treated as a source of uncertainty.
Abstract: This paper presents a model for the microgrid planning problem with uncertain physical and financial information. The microgrid planning problem investigates the economic viability of microgrid deployment and determines the optimal generation mix of distributed energy resources (DERs) for installation. Net metering is considered for exchanging power with the main grid and lowering the cost of unserved energy and DER investments. A robust optimization approach is adopted for considering forecast errors in load, variable renewable generation, and market prices. The microgrid islanding is further treated as a source of uncertainty. The microgrid planning problem is decomposed into an investment master problem and an operation subproblem. The optimal planning decisions determined in the master problem are employed in the subproblem to examine the optimality of the master solution by calculating the worst-case optimal operation under uncertain conditions. Optimality cuts sent to the master problem will govern subsequent iterations. Numerical simulations exhibit the effectiveness of the proposed model and further analyze the sensitivity of microgrid planning results on variety levels of uncertainty.
315 citations
TL;DR: This paper provides sufficient conditions under which the optimization problem can be solved via its convex relaxation, and demonstrates the operation of the algorithm, including its robustness against communication link failures, through several case studies involving 5-, 34-, and 123-bus power distribution systems.
Abstract: This paper addresses the problem of voltage regulation in power distribution networks with deep-penetration of distributed energy resources, e.g., renewable-based generation, and storage-capable loads such as plug-in hybrid electric vehicles. We cast the problem as an optimization program, where the objective is to minimize the losses in the network subject to constraints on bus voltage magnitudes, limits on active and reactive power injections, transmission line thermal limits and losses. We provide sufficient conditions under which the optimization problem can be solved via its convex relaxation. Using data from existing networks, we show that these sufficient conditions are expected to be satisfied by most networks. We also provide an efficient distributed algorithm to solve the problem. The algorithm adheres to a communication topology described by a graph that is the same as the graph that describes the electrical network topology. We illustrate the operation of the algorithm, including its robustness against communication link failures, through several case studies involving 5-, 34-, and 123-bus power distribution systems.
314 citations
TL;DR: This work proposes an algorithm to break-down the large problem size when many periods have to be considered, and the effectiveness of the approach and the significant benefits obtained by static and dynamic reconfiguration options in terms of DG hosting capacity are demonstrated using a modified benchmark distribution system.
Abstract: As the amount of distributed generation (DG) is growing worldwide, the need to increase the hosting capacity of distribution systems without reinforcements is becoming nowadays a major concern. This paper explores how the DG hosting capacity of active distribution systems can be increased by means of network reconfiguration, both static, i.e., grid reconfiguration at planning stage, and dynamic, i.e., grid reconfiguration using remotely controlled switches as an active network management (ANM) scheme. The problem is formulated as a mixed-integer, nonlinear, multi-period optimal power flow (MP-OPF) which aims to maximize the DG hosting capacity under thermal and voltage constraints. This work further proposes an algorithm to break-down the large problem size when many periods have to be considered. The effectiveness of the approach and the significant benefits obtained by static and dynamic reconfiguration options in terms of DG hosting capacity are demonstrated using a modified benchmark distribution system.
TL;DR: In this paper, a three-stage planning procedure is described to identify the optimal locations and parameters of distributed storage units, and the optimal operation of the storage units is simulated to quantify the benefits that they would provide by reducing congestion.
Abstract: Energy storage can alleviate the problems that the uncertainty and variability associated with renewable energy sources such as wind and solar create in power systems. Besides applications such as frequency control, temporal arbitrage or the provision of reserve, where the location of storage is not particularly relevant, distributed storage could also be used to alleviate congestion in the transmission network. In such cases, the siting and sizing of this distributed storage is of crucial importance to its cost-effectiveness. This paper describes a three-stage planning procedure to identify the optimal locations and parameters of distributed storage units. In the first stage, the optimal storage locations and parameters are determined for each day of the year individually. In the second stage, a number of storage units is available at the locations that were identified as being optimal in the first stage, and their optimal energy and power ratings are determined. Finally, in the third stage, with both the locations and ratings fixed, the optimal operation of the storage units is simulated to quantify the benefits that they would provide by reducing congestion. The quality of the final solution is assessed by comparing it with the solution obtained at the first stage without constraints on storage sites or size. The approach is numerically tested on the IEEE RTS 96.
TL;DR: In this article, a convex relaxation based on semidefinite programming (SDP) is shown to find a global solution of OPF for IEEE benchmark systems, and moreover this technique is guaranteed to work over acyclic (distribution) networks.
Abstract: This paper is concerned with the optimal power flow (OPF) problem. We have recently shown that a convex relaxation based on semidefinite programming (SDP) is able to find a global solution of OPF for IEEE benchmark systems, and moreover this technique is guaranteed to work over acyclic (distribution) networks. The present work studies the potential of the SDP relaxation for OPF over mesh (transmission) networks. First, we consider a simple class of cyclic systems, namely weakly-cyclic networks with cycles of size 3. We show that the success of the SDP relaxation depends on how the line capacities are modeled mathematically. More precisely, the SDP relaxation is proven to succeed if the capacity of each line is modeled in terms of bus voltage difference, as opposed to line active power, apparent power or angle difference. This result elucidates the role of the problem formulation. Our second contribution is to relate the rank of the minimum-rank solution of the SDP relaxation to the network topology. The goal is to understand how the computational complexity of OPF is related to the underlying topology of the power network. To this end, an upper bound is derived on the rank of the SDP solution, which is expected to be small in practice. A penalization method is then applied to the SDP relaxation to enforce the rank of its solution to become 1, leading to a near-optimal solution for OPF with a guaranteed optimality degree. The remarkable performance of this technique is demonstrated on IEEE systems with more than 7000 different cost functions.
TL;DR: In this paper, the problem of an aggregator bidding into the day-ahead electricity market with the objective of minimizing charging costs while satisfying the PEVs' flexible demand is addressed.
Abstract: With a large-scale introduction of plug-in electric vehicles (PEVs), a new entity, the PEV fleet aggregator, is expected to be responsible for managing the charging of, and for purchasing electricity for, the vehicles. We approach the problem of an aggregator bidding into the day-ahead electricity market with the objective of minimizing charging costs while satisfying the PEVs' flexible demand. The aggregator places demand bids only (no vehicle-to-grid is considered). The aggregator is assumed to potentially influence market prices, in contrast to what is commonly found in the literature. Specifically, the bidding strategy of the aggregator is formulated as a bilevel problem, which is implemented as a mixed-integer linear program. The upper level problem represents the charging cost minimization of the aggregator, whereas the lower level problem represents the market clearing. Since the bids of other market participants are not known to the aggregator ex ante, a simple strategy is used to estimate them, based on historical data. An aggregated representation of the PEV end-use requirements as a virtual battery, with time varying power and energy constraints, is proposed, derived from individual driving patterns. Since driving patterns cannot be perfectly forecasted, we introduce a probabilistic virtual battery model. We compare the results of the proposed bidding strategy with those of a bidding strategy assuming exogenous prices, uncontrolled charging, and a central dispatch of the fleet. We also explore the impacts of different sources of uncertainty. Results show that with the proposed bidding strategy, costs are significantly lower than under inflexible charging and are lower than assuming exogenous prices. Moreover, the approach also performs well under uncertainty. Results also suggest that the aggregator only has limited market power potential at moderate PEV penetrations.
TL;DR: In this article, the authors proposed a distribution locational marginal pricing (DLMP) method through quadratic programming (QP) designed to alleviate the congestion that might occur in a distribution network with high penetration of flexible demands.
Abstract: This paper presents the distribution locational marginal pricing (DLMP) method through quadratic programming (QP) designed to alleviate the congestion that might occur in a distribution network with high penetration of flexible demands. In the DLMP method, the distribution system operator (DSO) calculates dynamic tariffs and publishes them to the aggregators, who make the optimal energy plans for the flexible demands. The DLMP through QP instead of linear programing as studied in previous literatures solves the multiple solution issue of the aggregator optimization which may cause the decentralized congestion management by DLMP to fail. It is proven in this paper, using convex optimization theory, the aggregator's optimization problem through QP is strictly convex and has a unique solution. The Karush-Kuhn-Tucker (KKT) conditions and the unique solution of the aggregator optimization ensure that the centralized DSO optimization and the decentralized aggregator optimization converge. Case studies using a distribution network with high penetration of electric vehicles (EVs) and heat pumps (HPs) validate the equivalence of the two optimization setups, and the efficacy of the proposed DLMP through QP for congestion management.
TL;DR: In this article, the authors proposed an optimization framework for the operating model of battery swapping stations, which considers the day-ahead scheduling process and uses inventory robust optimization and multi-band robust optimization to model electricity price uncertainty.
Abstract: For a successful rollout of electric vehicles (EVs), it is required to establish an adequate charging infrastructure. The adequate access to such infrastructure would help to mitigate concerns associated with limited EV range and long charging times. Battery swapping stations are poised as effective means of eliminating the long waiting times associated with charging the EV batteries. These stations are mediators between the power system and their customers. In order to successfully deploy this type of stations, a business and operating model is required, that will allow it to generate profits while offering a fast and reliable alternative to charging batteries. This paper proposes an optimization framework for the operating model of battery swapping stations. The proposed model considers the day-ahead scheduling process. Battery demand uncertainty is modeled using inventory robust optimization, while multi-band robust optimization is employed to model electricity price uncertainty. The results show the viability of the proposed model as a business case, as well as the effectiveness of the model to provide the required service.
TL;DR: In this paper, a hybrid multi-objective particle swarm optimization (HMOPSO) approach is proposed to minimize the power system cost and improve the system voltage profiles by searching sitting and sizing of storage units under consideration of uncertainties in wind power production.
Abstract: Energy storage systems play a significant role in both distributed power systems and utility power systems Among the many benefits of an energy storage system, the improvement of power system cost and voltage profile can be the salient specifications of storage systems Studies show that improper size and placement of energy storage units leads to undesired power system cost as well as the risk of voltage stability, especially in the case of high renewable energy penetration To solve the problem, a hybrid multi-objective particle swarm optimization (HMOPSO) approach is proposed in the paper to minimize the power system cost and improve the system voltage profiles by searching sitting and sizing of storage units under consideration of uncertainties in wind power production Furthermore, the probability cost analysis is first put forward in this paper The proposed HMOPSO combines multi-objective particle swarm optimization (MOPSO) algorithm with elitist nondominated sorting genetic algorithm (NSGA-II) and probabilistic load flow technique It also incorporates a five-point estimation method (5PEM) for discretizing wind power distribution The IEEE 30-bus system is adopted to perform case studies The simulation results for each case clearly demonstrate the necessity for optimal storage allocation, and the effectiveness of the proposed method
TL;DR: The obtained results show that the supplementary control is able to properly damp the sub-synchronous oscillations of DFIG wind turbines by updating the existing DFIG control systems without the inclusion of expensive additional damping devices, and reducing the risk of wind generation tripping.
Abstract: This paper presents a damping control to mitigate sub-synchronous interactions (SSI) in doubly-fed induction generator (DFIG) wind turbines connected to series-compensated lines. This issue has gained attention due to the recent SSI phenomena reported in DFIG wind farms located near series capacitors. Two approaches which add a supplementary damping control signal are compared: one of them, integrated to the grid-side converter, and the other one, to the rotor-side converter. The SSI damping controls are designed using a multi-input multi-output state-space methodology. This allows to easily tune a high performance controller using several measurements and control inputs. Small- and large-signal stability analyses, robustness aspects, impact of the supplementary controls on the system modes, and influence of different operating conditions on the SSI are also discussed. The obtained results show that the supplementary control is able to properly damp the sub-synchronous oscillations of DFIG wind turbines by updating the existing DFIG control systems without the inclusion of expensive additional damping devices, and reducing the risk of wind generation tripping.
TL;DR: In this paper, the optimal storage management and sizing problem in the presence of renewable energy and dynamic pricing associated with electricity from the grid is formulated as a stochastic dynamic program that aims to minimize the long-run average cost of electricity used and investment in storage, if any, while satisfying all the demand.
Abstract: We address the optimal energy storage management and sizing problem in the presence of renewable energy and dynamic pricing associated with electricity from the grid. We formulate the problem as a stochastic dynamic program that aims to minimize the long-run average cost of electricity used and investment in storage, if any, while satisfying all the demand. We model storage with ramp constraints, conversion losses, dissipation losses and an investment cost. We prove the existence of an optimal storage management policy under mild assumptions and show that it has a dual threshold structure. Under this policy, we derive structural results, which indicate that the marginal value from storage decreases with its size and that the optimal storage size can be computed efficiently. We prove a rather surprising result, as we characterize the maximum value of storage under constant prices and i.i.d. net-demand processes: if the storage is a profitable investment, then the ratio of the amortized cost of storage to the constant price is less than 1/4. We further perform sensitivity analysis on the size of optimal storage and its gain via a case study. Finally, with a computational study on real data, we demonstrate significant savings with energy storage.
TL;DR: In this paper, the authors proposed a centralized supplementary frequency regulation (SFR) of interconnected power systems, where an aggregator calculates the total frequency regulation capacity (FRC) and expected V2G (EV2G) power of EVs based on the data communicated between the aggregator and individual EVs or EV charging stations.
Abstract: Electric vehicles (EVs) as distributed storage devices have the potential to provide frequency regulation services due to the fast adjustment of charging/discharging power. In our previous research, decentralized vehicle-to-grid (V2G) control methods for EVs were proposed to participate in primary frequency control. In this paper, our attention is on bringing a large number of EVs into the centralized supplementary frequency regulation (SFR) of interconnected power systems. An aggregator is the coordinator between EVs and the power system control center. The aggregator calculates the total frequency regulation capacity (FRC) and expected V2G (EV2G) power of EVs based on the data communicated between the aggregator and individual EVs or EV charging stations. With FRC and EV2G power, a V2G control strategy is proposed for the aggregator to dispatch regulation requirements to EVs and EV charging stations. In individual EV charging stations, the FRC is calculated on the basis of the V2G power at present time, and EV2G power is presented considering both frequency regulation and charging demands. Besides, V2G control strategies are developed to distribute regulation requirements to each EV. Simulations on an interconnected power grid based on a practical power grid in China have demonstrated the effectiveness of the proposed strategies.
TL;DR: This paper proposed a multi-agent system based distributed control solution that can realize optimal generation control and is designed based upon an improved distributed gradient algorithm, which can address both equality and inequality constraints.
Abstract: In traditional power system, economic dispatch and generation control are separately applied. Online generation adjustment is necessary to regulate generation reference for real-time control to realize economic operation of power systems. Since most economic dispatch solutions are centralized, they are usually expensive to implement, susceptible to single-point-failures, and inflexible. To address the above-mentioned problems, this paper proposed a multi-agent system based distributed control solution that can realize optimal generation control. The solution is designed based upon an improved distributed gradient algorithm, which can address both equality and inequality constraints. To improve the reliability of multi-agent system, the N-1 rule is introduced to design the communication network topology. Compared with centralized solutions, the distributed control solution not only can achieve comparable solutions but also can respond timely when the system experiences change of operating conditions. MAS based real-time simulation results demonstrate the effectiveness of the proposed solution.
TL;DR: In this paper, the authors investigate the potential for aggregations of residential thermostatically controlled loads (TCLs) such as air conditioners, to arbitrage intraday wholesale electricity market prices via non-disruptive load control.
Abstract: We investigate the potential for aggregations of residential thermostatically controlled loads (TCLs), such as air conditioners, to arbitrage intraday wholesale electricity market prices via non-disruptive load control. We present two arbitrage approaches: 1) a benchmark that gives us an optimal policy but requires local computation or real-time communication and 2) an alternative based on a thermal energy storage model, which relies on less computation/communication infrastructure, but is suboptimal. We find that the alternative approach achieves around 60%–80% of the optimal wholesale energy cost savings. We use this approach to compute practical upper bounds for savings via arbitrage with air conditioners in California's intraday energy market. We investigate six sites over four years and find that the savings range from $2–$37 per TCL per year, and depend upon outdoor temperature statistics and price volatility.
TL;DR: In this article, the authors compared the validity of a typical DC power flow-based CFS in cascading failure analysis with a new numerical metric defined as the critical moment (CM).
Abstract: When the modern electrical infrastructure is undergoing a migration to the Smart Grid, vulnerability and security concerns have also been raised regarding the cascading failure threats in this interconnected transmission system with complex communication and control challenge. The DC power flow-based model has been a popular model to study the cascading failure problem due to its efficiency, simplicity and scalability in simulations of such failures. However, due to the complex nature of the power system and cascading failures, the underlying assumptions in DC power flow-based cascading failure simulators (CFS) may fail to hold during the development of cascading failures. This paper compares the validity of a typical DC power flow-based CFS in cascading failure analysis with a new numerical metric defined as the critical moment (CM). The adopted CFS is first implemented to simulate system behavior after initial contingencies and to evaluate the utility of DC-CFS in cascading failure analysis. Then the DC-CFS is compared against another classic, more precise power system stability methodology, i.e., the transient stability analysis (TSA). The CM is introduced with a case study to assess the utilization of these two models for cascading failure analysis. Comparative simulations on the IEEE 39-bus and 68-bus benchmark reveal important consistency and discrepancy between these two approaches. Some suggestions are provided for using these two models in the power grid cascading failure analysis.
TL;DR: The results of case studies show that FDSNP is effective in diagnosing faults in power transmission networks for single and multiple fault situations with/without incomplete and uncertain SCADA data, and is superior to four methods reported in the literature in terms of the correctness of diagnosis results.
Abstract: This paper proposes a graphic modeling approach, fault diagnosis method based on fuzzy reasoning spiking neural P systems (FDSNP), for power transmission networks. In FDSNP, fuzzy reasoning spiking neural P systems (FRSN P systems) with trapezoidal fuzzy numbers are used to model candidate faulty sections and an algebraic fuzzy reasoning algorithm is introduced to obtain confidence levels of candidate faulty sections, so as to identify faulty sections. FDSNP offers an intuitive illustration based on a strictly mathematical expression, a good fault-tolerant capacity due to its handling of incomplete and uncertain messages in a parallel manner, a good description for the relationships between protective devices and faults, and an understandable diagnosis model-building process. To test the validity and feasibility of FDSNP, seven cases of a local subsystem in an electrical power system are used. The results of case studies show that FDSNP is effective in diagnosing faults in power transmission networks for single and multiple fault situations with/without incomplete and uncertain SCADA data, and is superior to four methods, reported in the literature, in terms of the correctness of diagnosis results.
TL;DR: In this article, moment relaxations are developed from the Lasserre hierarchy for solving generalized moment problems, and they are capable of globally solving many small OPF problems for which existing relaxations fail.
Abstract: Convex relaxations of non-convex optimal power flow (OPF) problems have recently attracted significant interest. While existing relaxations globally solve many OPF problems, there are practical problems for which existing relaxations fail to yield physically meaningful solutions. This paper applies moment relaxations to solve many of these OPF problems. The moment relaxations are developed from the Lasserre hierarchy for solving generalized moment problems. Increasing the relaxation order in this hierarchy results in “tighter” relaxations at the computational cost of larger semidefinite programs. Low-order moment relaxations are capable of globally solving many small OPF problems for which existing relaxations fail. By exploiting sparsity and only applying the higher-order relaxation to specific buses, global solutions to larger problems are computationally tractable through the use of an iterative algorithm informed by a heuristic for choosing where to apply the higher-order constraints. With standard semidefinite programming solvers, the algorithm globally solves many test systems with up to 300 buses for which the existing semidefinite relaxation fails to yield globally optimal solutions.
TL;DR: In this article, an advanced vector current control for a voltage source converter (VSC) connected to a weak grid is proposed, which permits high-performance regulation of the active power and the voltage for the feasible VSC range of operation.
Abstract: This paper addresses an advanced vector current control for a voltage source converter (VSC) connected to a weak grid. The proposed control methodology permits high-performance regulation of the active power and the voltage for the feasible VSC range of operation. First, the steady state characteristics for a power converter connected to a very weak system with a short circuit ratio (SCR) of 1 are discussed in order to identify the system limits. Then, the conventional vector control (inner loop) and the conventional power/voltage control (outer loop) stability and frequency responses are analyzed. From the analysis of the classic structure, an enhanced outer loop based on a decoupled and gain-scheduling controller is presented and its stability is analyzed. The proposed control is validated by means of dynamic simulations and it is compared with classic vector current control. Simulation results illustrate that the proposed control system could provide a promising approach to tackle the challenging problem of VSC in connection with weak AC grids.
TL;DR: In this article, the authors proposed a multi-scale cooling and electricity coordinated schedule for optimal microgrid operation, which achieves an integrated optimization for multi-energy type supply, and makes the MG be controllable as seen from the main grid.
Abstract: For optimal microgrid (MG) operation, one challenge is the supply of cooling and electricity energy is a coupled co-optimization issue when considering the combined cooling, heating and power (CCHP) units and ice-storage air-conditioners. Another challenge is the inherent randomness of renewable energy within the MG should be accommodated by MG itself. In Part I of this two-part paper, the partial load performance of CCHPs and the performance of ice-storage air-conditioners are modeled, and the cooling and electricity coordinated MG day-ahead scheduling and real-time dispatching models are established. In day-ahead scheduling model, the uncertainty of wind and solar power is represented by multi-scenarios and the objective is to achieve the minimal expected MG operation cost. In real-time dispatching model, the different time-scale dispatch schemes are respectively applied for cooling and electricity to smooth out the fluctuations of renewable energy supply and to follow the variations of cooling and electricity demands by the fine dispatching of the components within MG such that the impact of MG to the connected main grid is minimal. The proposed MG multi time-scale cooling and electricity coordinated schedule achieves an integrated optimization for multi energy-type supply, and makes the MG be controllable as seen from the main grid.
TL;DR: In this article, the authors address the multistage expansion planning problem of a distribution system where investments in the distribution network and in distributed generation are jointly considered, and the resulting optimization problem is a mixed-integer linear program for which finite convergence to optimality is guaranteed and efficient off-the-shelf software is available.
Abstract: This paper addresses the multistage expansion planning problem of a distribution system where investments in the distribution network and in distributed generation are jointly considered. Network expansion comprises several alternatives for feeders and transformers. Analogously, the installation of distributed generation takes into account several alternatives for conventional and wind generators. Unlike what is customarily done, a set of candidate nodes for generator installation is considered. Thus, the optimal expansion plan identifies the best alternative, location, and installation time for the candidate assets. The model is driven by the minimization of the net present value of the total cost including the costs related to investment, maintenance, production, losses, and unserved energy. The costs of energy losses are modeled by a piecewise linear approximation. As another distinctive feature, radiality conditions are specifically tailored to accommodate the presence of distributed generation in order to avoid the isolation of distributed generators and the issues associated with transfer nodes. The resulting optimization problem is a mixed-integer linear program for which finite convergence to optimality is guaranteed and efficient off-the-shelf software is available. Numerical results illustrate the effective performance of the proposed approach.
TL;DR: In this paper, a sparse formulation and solution for the affinely adjustable robust counterpart (AARC) of the multi-period OPF problem is presented, which aims at operating a storage portfolio via receding horizon control; it computes the optimal base-point conventional generation and storage schedule for the forecasted load and renewable generation, together with the constrained participation factors that dictate how conventional generator and storage will adjust to maintain feasible operation whenever the renewables deviate from their forecast.
Abstract: Renewable energy sources and energy storage systems present specific challenges to the traditional optimal power flow (OPF) paradigm. First, storage devices require the OPF to model charge/discharge dynamics and the supply of generated power at a later time. Second, renewable energy sources necessitate that the OPF solution accounts for the control of conventional power generators in response to errors of renewable power forecast, which are significantly larger than the traditional load forecast errors. This paper presents a sparse formulation and solution for the affinely adjustable robust counterpart (AARC) of the multi-period OPF problem. The AARC aims at operating a storage portfolio via receding horizon control; it computes the optimal base-point conventional generation and storage schedule for the forecasted load and renewable generation, together with the constrained participation factors that dictate how conventional generation and storage will adjust to maintain feasible operation whenever the renewables deviate from their forecast. The approach is demonstrated on standard IEEE networks dispatched over a 24-h horizon with interval forecasted wind power, and the feasibility of operation under interval uncertainty is validated via Monte Carlo analysis. The computational performance of the proposed approach is compared with a conventional implementation of the AARC that employs successive constraint enforcement.
TL;DR: In this article, a stochastic reactive power compensation scheme was developed to account for the increasing time-variability of distributed generation and demand, and an online reactive control scheme was devised to track variations in solar generation and household demand.
Abstract: Distribution microgrids are being challenged by reverse power flows and voltage fluctuations due to renewable generation, demand response, and electric vehicles Advances in photovoltaic (PV) inverters offer new opportunities for reactive power management provided PV owners have the right investment incentives In this context, reactive power compensation is considered here as an ancillary service Accounting for the increasing time-variability of distributed generation and demand, a stochastic reactive power compensation scheme is developed Given uncertain active power injections, an online reactive control scheme is devised This scheme is distribution-free and relies solely on power injection data Reactive injections are updated using the Lagrange multipliers of a second-order cone program Numerical tests on an industrial 47-bus microgrid and the residential IEEE 123-bus feeder corroborate the reactive power management efficiency of the novel stochastic scheme over its deterministic alternative, as well as its capability to track variations in solar generation and household demand