Showing papers in "IEEE Transactions on Sustainable Energy in 2016"
TL;DR: In this paper, a maximum power point tracking (MPPT) design for a photovoltaic (PV) system using a grey wolf optimization (GWO) technique is presented, where the problem of tracking the global peak (GP) of a PV array under partial shading conditions (PSCs) is attempted employing the GWO-based MPPT technique.
Abstract: This paper presents a maximum power point tracking (MPPT) design for a photovoltaic (PV) system using a grey wolf optimization (GWO) technique. The GWO is a new optimization method which overcomes the limitations such as lower tracking efficiency, steady-state oscillations, and transients as encountered in perturb and observe (P&O) and improved PSO (IPSO) techniques. The problem of tracking the global peak (GP) of a PV array under partial shading conditions (PSCs) is attempted employing the GWO-based MPPT technique. The proposed scheme is studied for a PV array under PSCs which exhibits multiple peaks and its tracking performance is compared with that of two MPPT algorithms, namely P&O-MPPT and IPSO-MPPT. The proposed GWO-MPPT algorithm is implemented on a PV system using MATLAB/SIMULINK. Furthermore, an experimental setup is developed to verify the efficacy of the proposed system. From the obtained simulation and experimental results, it is observed that the proposed MPPT algorithm outperforms both P&O and IPSO MPPTs.
555 citations
TL;DR: In this article, a combined heat and power dispatch (CHPD) is formulated to coordinate the operation of electric power system (EPS) and district heating system (DHS), which is solved by an iterative method.
Abstract: The regional integration of variable wind power could be restricted by a strong coupling of electric power generation dispatch and heat supply of combined heat-and-power (CHP) units. The coupling in cold seasons precludes CHPs from providing the necessary flexibility for managing the wind power dispatch. The lack of flexibility problem can be tackled by exploiting the energy storage capability of a district heating network (DHN) which decouples the strong linkage of electric power and heat supplies. In this paper, a combined heat and power dispatch (CHPD) is formulated to coordinate the operation of electric power system (EPS) and district heating system (DHS). The proposed CHPD model which is solved by an iterative method considers the temperature dynamics of DHN for exploiting energy storage as an option for managing the variability of wind energy. The simulation results are discussed for several test systems to demonstrate the potential benefits of the proposed method in terms of operation economics, wind power utilization, as well as the potential benefits for real systems.
544 citations
TL;DR: In this article, a modified P&O was proposed to reduce the steady-state oscillation and mitigate the probability of losing the tracking direction of the perturb and observed (P&O)-based maximum power point tracking (MPPT) for PV system.
Abstract: This paper proposes a method to reduce the steady-state oscillation and to mitigate the probability of losing the tracking direction of the perturb and observed (P&O)-based maximum power point tracking (MPPT) for PV system. The modified scheme retains the conventional P&O structure, but with a unique technique to dynamically alter the perturbation size. At the same time, a dynamic boundary condition is introduced to ensure that the algorithm will not diverge from its tracking locus. The modified P&O is simulated in MATLAB Simulink and its performance is benchmarked using the standard MPPT efficiency ${{\boldsymbol{\eta }}_{MPPT}}$ calculation. Furthermore, the proposed concept is validated experimentally using a buck-boost converter, fed by a solar PV array simulator (PVAS). Based on the EN 50530 dynamic irradiance tests, the proposed method achieved an average ${{\boldsymbol{\eta }}_{MPPT}}$ almost 1.1% higher than the conventional P&O when irradiance changes slowly and about 12% higher under fast change of irradiance.
320 citations
TL;DR: In this article, transmission-constrained unit commitment (UC) with combined electricity and district heating networks (UC-CEHN) is formulated with a linear DHN model to coordinate short-term operation of electric power and heating systems.
Abstract: Wind power integration could be restricted by inflexible operation of combined heat and power (CHP) units due to the strong linkage between power generation and heating supply in winter. Utilization of the heat storage capacity of existing district heating network (DHN) is a cost-effective measure to enhance power system operational flexibility to accommodate large amounts of variable wind power. In this paper, transmission-constrained unit commitment (UC) with combined electricity and district heating networks (UC-CEHN) is formulated with a linear DHN model to coordinate short-term operation of electric power and district heating systems. The heat storage capacity of the DHN is modeled by capturing the quasi-dynamics of pipeline temperature. Both deterministic and robust models are developed to incorporate UC with the linear DHN model. Case studies are carried out for two test systems to show the potential benefits of the proposed method in terms of wind power integration and efficient operation.
307 citations
TL;DR: Wang et al. as discussed by the authors proposed a two-stage robust optimization model to coordinate the discrete and continuous reactive power compensators and find a robust optimal solution that can hedge against any possible realization within the uncertain wind power output.
Abstract: Traditional reactive power optimization aims to minimize the total transmission losses by control reactive power compensators and transformer tap ratios, while guaranteeing the physical and operating constraints, such as voltage magnitudes and branch currents to be within their reasonable range. However, large amounts of renewable resources coming into power systems bring about great challenges to traditional planning and operation due to the stochastic nature. In most of the practical cases from China, the wind farms are centrally integrated into active distribution networks. By the use of conic relaxation based branch flow formulation, the reactive optimization problem in active distribution networks can be formulated as a mixed integer convex programming model that can be tractably dealt with. Furthermore, to address the uncertainties of wind power output, a two-stage robust optimization model is proposed to coordinate the discrete and continuous reactive power compensators and find a robust optimal solution that can hedge against any possible realization within the uncertain wind power output. Moreover, the second order cone programming-based column-and-constraint generation algorithm is employed to solve the proposed two-stage robust reactive power optimization model. Numerical results on 33-, 69- and 123-bus systems and comparison with the deterministic approach demonstrate the effectiveness of the proposed method.
290 citations
TL;DR: In this paper, the authors present a comprehensive review of power electronics (PE) topologies for utility BESS that have been proposed either within industry or the academic literature, and a comparison of the presently most commercially viable topologies is conducted in terms of estimated power conversion efficiency and relative cost.
Abstract: The increasing penetration of renewable energy sources (RES) poses a major challenge to the operation of the electricity grid owing to the intermittent nature of their power output. The ability of utility-scale battery energy storage systems (BESS) to provide grid support and smooth the output of RES in combination with their decrease in cost has fueled research interest in this technology over the last couple of years. Power electronics (PE) is the key enabling technology for connecting utility-scale BESS to the medium-voltage grid. PE ensure energy is delivered while complying with grid codes and dispatch orders. Simultaneously, the PE must regulate the operating point of the batteries, thus for instance preventing overcharge of batteries. This paper presents a comprehensive review of PE topologies for utility BESS that have been proposed either within industry or the academic literature. Moreover, a comparison of the presently most commercially viable topologies is conducted in terms of estimated power conversion efficiency and relative cost.
272 citations
TL;DR: In this article, a solar power prediction model based on various satellite images and a support vector machine (SVM) learning scheme was proposed to forecast the motion vectors of clouds by utilizing satellite images of atmospheric motion vectors (AMVs).
Abstract: Penetration of solar energy into main grid has gradually increased in recent years due to a growing number of large-scale photovoltaic (PV) farms. The power output of these PV farms may fluctuate due to a wide variability of meteorological conditions, and, thus, we need to compensate for this effect in advance. In this paper, we propose a solar power prediction model based on various satellite images and a support vector machine (SVM) learning scheme. The motion vectors of clouds are forecasted by utilizing satellite images of atmospheric motion vectors (AMVs). We analyze 4 years’ historical satellite images and utilize them to configure a large number of input and output data sets for the SVM learning. We compare the performance of the proposed SVM-based model, the conventional time-series model, and an artificial neural network (ANN) model in terms of prediction accuracy.
250 citations
TL;DR: In this article, the use of a least mean fourth (LMF)-based algorithm for single-stage three-phase grid-integrated solar photovoltaic (SPV) system is proposed.
Abstract: This paper proposes the use of a least mean fourth (LMF)-based algorithm for single-stage three-phase grid-integrated solar photovoltaic (SPV) system. It consists of an SPV array, voltage source converter (VSC), three-phase grid, and linear/nonlinear loads. This system has an SPV array coupled with a VSC to provide three-phase active power and also acts as a static compensator for the reactive power compensation. It also conforms to an IEEE-519 standard on harmonics by improving the quality of power in the three-phase distribution network. Therefore, this system serves to provide harmonics alleviation, load balancing, power factor correction and regulating the terminal voltage at the point of common coupling. In order to increase the efficiency and maximum power to be extracted from the SPV array at varying environmental conditions, a single-stage system is used along with perturb and observe method of maximum power point tracking (MPPT) integrated with the LMF-based control technique. The proposed system is modeled and simulated using MATLAB/Simulink with available simpower system toolbox and the behaviour of the system under different loads and environmental conditions are verified experimentally on a developed system in the laboratory.
237 citations
TL;DR: In this paper, a multi-stage integrated gas and electrical transmission network model is proposed to quantify the flexibility the gas network can provide to the power system, as well as the constraints it may impose on it, with also considering different heating scenarios.
Abstract: In power systems with more and more variable renewable sources, gas generation is playing an increasingly prominent role in providing short-term flexibility to meet net-load requirements. The flexibility provided by the gas turbines in turn relies on the flexibility of the gas network. While there are several discussions on the ability of the gas network in providing this operational flexibility, this has not been clearly modeled or quantified. In addition, the gas network may also be responsible for supplying heating technologies, and low-carbon scenarios see a tighter interaction between the electricity, heating and gas sectors, which calls for a holistic multi-energy system assessment. On these premises, this paper presents an original methodology to quantify the flexibility the gas network can provide to the power system, as well as the constraints it may impose on it, with also consideration of different heating scenarios. This is achieved by a novel multi-stage integrated gas and electrical transmission network model, which uses electrical DC OPF and both steady-state and transient gas analyses. A novel metric that makes use of the concept of zonal linepack is also introduced to assess the integrated gas and electrical flexibility, which is then used to impose gas-related inter-network inter-temporal constraints on the electrical OPF. Case studies are performed for the Great Britain transmission system for different renewables and heating scenarios to demonstrate the proposed integrated flexibility assessment methodology, provide insights into the effects of changes to the heating sector on the multi-energy system’s combined flexibility requirements and capability, and assess how the electrical network can experience local generation and reserve constraints related to the gas network’s lack of flexibility.
236 citations
TL;DR: In this article, a set of analytical expressions is introduced to determine the five parameters of the single-diode model for crystalline PV modules at any operating conditions, in a simple and straightforward manner.
Abstract: Determination of PV model parameters usually requires time consuming iterative procedures, prone to initialization and convergence difficulties. In this paper, a set of analytical expressions is introduced to determine the five parameters of the single-diode model for crystalline PV modules at any operating conditions, in a simple and straightforward manner. The derivation of these equations is based on a newly found relation between the diode ideality factor and the open circuit voltage, which is explicitly formulated using the temperature coefficients. The proposed extraction method is robust, cost-efficient, and easy-to-implement, as it relies only on datasheet information, while it is based on a solid theoretical background. Its accuracy and computational efficiency is verified and compared to other methods available in the literature through both simulation and outdoor measurements.
207 citations
TL;DR: In this paper, a cost-based multiobjective optimization strategy for optimal integration of battery energy storage systems (BESSs) to improve the load and distributed generation (DG) hosting ability of the utility grid is presented.
Abstract: This paper proposes a strategy for optimal integration of battery energy storage systems (BESSs) to improve the load and distributed generation (DG) hosting ability of the utility grid. An effective tool that determines the optimal capacity and day-ahead operation strategy for deployment of distribution network operator (DNO)-controlled BESSs is presented. It is a cost-based multiobjective optimization strategy that considers two primary factors: 1)distribution system cost; and 2)battery cycling cost. Quantitative analyses on the benefits and tradeoffs of BESS installations are carried out considering different service options. BESS is investigated for three main service options: 1)voltage regulation; 2)loss reduction; and 3)peak reduction. The performance and benefits of the optimized BESS to control one service option exclusively or multiple services simultaneously is compared. The analysis is further extended to study the effect of installation site on the size, management strategy, and the service option. Results show that optimal integration of BESSs can realize maximum operational and cost benefits while effectively elevating the load and DG hosting capability of the network. The approach is developed using MATLAB interior-point algorithm. Simulations are conducted for the medium voltage (MV) IEEE 33 bus system and a low voltage (LV) distribution network in Western Australia studied during the Perth Solar City Trial.
TL;DR: In this article, the authors proposed a simple and effective modifications to the conventional method (Newton Raphson) to compute the power flow for micro-grids, which can be easily integrated in current commercially available power system software and can be applied for power system studies.
Abstract: The study of power flow analysis for microgrids has gained importance where several methods have been proposed to solve these problems. However, these schemes are complicated and not easy to implement due to the absence of a slack bus as well as the dependence of the power on frequency as a result of the droop characteristics. This paper proposes simple and effective modifications to the conventional method (Newton Raphson) to compute the power flow for microgrids. The presented method provides a simple, easy to implement, and accurate approach to solve the power flow equations for microgrids. The proposed method is applied to two test systems: a 6-bus system and a 38-bus system. The results are compared against simulation results from PSCAD/EMTDC which validate the effectiveness of the developed method. The proposed technique can be easily integrated in current commercially available power system software and can be applied for power system studies.
TL;DR: In this paper, the authors proposed an MHC evaluation method while considering the robust optimal operation of on load tap changers (OLTCs) and static var compensators (SVCs) in the uncertain context of DG power outputs and load consumptions.
Abstract: With the rapidly increasing penetration of renewable distributed generation (DG), the maximum hosting capacity (MHC) of a distribution system has become a major concern. To effectively evaluate the ability of a distribution system to accommodate DGs, this paper proposes an MHC evaluation method while considering the robust optimal operation of on load tap changers (OLTCs) and static var compensators (SVCs) in the uncertain context of DG power outputs and load consumptions. The proposed method determines the DG hosting capacities corresponding to different conservative levels. Furthermore, this paper discusses how to find out the most critical technical constraint that may limit the MHC by adjusting parameters of the proposed robust formulation. The effectiveness of the proposed method is demonstrated using a modified IEEE 33-bus distribution system.
TL;DR: In this paper, the authors presented an advanced control strategy for the optimal microgrid operation using a two-layer model predictive method. But, the authors did not consider the impact of unpredictable variations in load demand or additional power supply from renewable sources.
Abstract: Microgrids consisting of diesel generators, storage devices, and renewable sources present an effective approach for an economic energy supply to rural areas. Advanced control methods are needed to improve the energy dispatch, enable a cost-efficient operation and guarantee an uninterrupted power supply. In particular, sudden variations in load demand or additional power supply from renewable sources are often unpredictable and underline the need for enhanced control. This paper presents an advanced control strategy for the optimal microgrid operation using a two-layer model predictive method. The first optimization layer presents an optimal control problem, based on real-time predictions of future power profiles, for the calculation of the optimal energy dispatch. To improve the robustness of the control strategy toward prediction errors, a boundary value problem is solved to adjust the diesel generator power in the second stage. The model predictive control framework is further used to adapt the weights of the forecast algorithm. Simulation studies are carried out by using real-world data to illustrate the performance and economic benefits of the proposed method. Results show the effectiveness of the control strategy in terms of computational feasibility, accuracy, increased robustness, and reduced cost.
TL;DR: In this paper, a two-stage distributionally robust optimization model for the joint energy and reserve dispatch (D-RERD for short) of bulk power systems with significant renewable energy penetration is proposed.
Abstract: This paper proposes a two-stage distributionally robust optimization model for the joint energy and reserve dispatch (D-RERD for short) of bulk power systems with significant renewable energy penetration. Distinguished from the prevalent uncertainty set-based and worst-case scenario oriented robust optimization methodology, we assume that the output of volatile renewable generation follows some ambiguous distribution with known expectations and variances, the probability distribution function (pdf) is restricted in a functional uncertainty set. D-RERD aims at minimizing the total expected production cost in the worst renewable power distribution. In this way, D-RERD inherits the advantages from both stochastic optimization and robust optimization: statistical characteristic is taken into account in a data-driven manner without requiring the exact pdf of uncertain factors. We present a convex optimization-based algorithm to solve the D-RERD, which involves solving semidefinite programming (SDP), convex quadratic programming (CQP), and linear programming (LP). The performance of the proposed approach is compared with the emerging adaptive robust optimization (ARO)-based model on the IEEE 118-bus system. Their respective features are discussed in case studies.
TL;DR: In this article, the degradation of electric vehicle (EV) lithium-ion batteries in vehicle-to-grid (V2G) programs is investigated and a practical wear cost model for EVs charge scheduling applications is proposed.
Abstract: This paper concentrates on degradation of electric vehicle (EV) lithium-ion batteries in vehicle-to-grid (V2G) programs and proposes a practical wear cost model for EVs charge scheduling applications. As the first step, all the factors affecting the cycle life of lithium-ion batteries are identified and their impacts on degradation process are investigated. Subsequently, a general model for battery loss of cycle life is devised incorporating all the pertinent factors associated with charging and discharging activities in V2G applications. Modeling the battery wear cost as a series of equal-payments over the cycle life, a mechanism for calculating the cost incurred by EV users due to participation in V2G programs is developed. Taking into account the developed battery degradation cost model, EVs charge scheduling problem is revisited and it is formulated as a mixed integer linear programming problem. As the actual battery degradation cost and adopted charging strategy are mutually dependent, a novel iterative method is proposed to efficiently obtain the optimal solution to charge scheduling problem and calculate the associated wear price. Several case studies are presented to demonstrate the effectiveness and applicability of the proposed method in integrating the degradation cost of lithium-ion batteries into charge scheduling of V2G-capable EVs.
TL;DR: A stochastic optimization model for optimal bidding strategies of electric vehicle (EV) aggregators in day-ahead energy and ancillary services markets with variable wind energy and a game theoretic approach is developed for analyzing the competition among the EV aggregators.
Abstract: This paper proposes a stochastic optimization model for optimal bidding strategies of electric vehicle (EV) aggregators in day-ahead energy and ancillary services markets with variable wind energy. The forecast errors of EV fleet characteristics, hourly loads, and wind energy as well as random outages of generating units and transmission lines are considered as potential uncertainties, which are represented by scenarios in the Monte Carlo Simulation (MCS). The conditional value at risk (CVaR) index is utilized for measuring EV aggregators risks caused by the uncertainties. The EV aggregators optimal bidding strategy is formulated as a mathematical programming with equilibrium constraints (MPEC), in which the upper level problem is the aggregators CVaR maximization while the lower level problem corresponds to the system operation cost minimization. The bi-level problem is transformed into a single-level mixed integer linear programming (MILP) problem using the prime-dual formulation with linearized constraints. The progressive hedging algorithm (PHA) is utilized to solve the resulting single-level MILP problem. A game theoretic approach is developed for analyzing the competition among the EV aggregators. Numerical cases are studied for a modified 6-bus system and the IEEE 118-bus system. The results show the validity of the proposed approach and the impact of the aggregators bidding strategies on the stochastic electricity market operation.
TL;DR: In this paper, a releasable kinetic energy (KE)-based gain scheme was proposed for a doubly fed induction generator (DFIG) WPP that differentiates the contributions of the WTGs depending on their stored KE.
Abstract: Wind turbine generators (WTGs) in a wind power plant (WPP) contain different levels of releasable kinetic energy (KE) because of the wake effects. This paper proposes a releasable KE-based inertial control scheme for a doubly fed induction generator (DFIG) WPP that differentiates the contributions of the WTGs depending on their stored KE. The proposed KE-based gain scheme aims to make use of the releasable KE in a WPP to raise the frequency nadir. To achieve this, two additional loops for the inertial control are implemented in each DFIG controller: the rate of change of frequency and droop loops. The proposed scheme adjusts the two loop gains in a DFIG controller depending on its rotor speed so that a DFIG operating at a higher rotor speed releases more KE. The performance of the proposed scheme was investigated under various wind conditions. The results clearly indicate that the proposed scheme successfully improves the frequency nadir more than the conventional same gain scheme by releasing more KE stored in a WPP, and it helps all WTGs to ensure stable operation during inertial control by avoiding the rotor speed reaching the minimum speed limit.
TL;DR: In this paper, the effect of wind power on frequency regulation capability at different penetration levels is examined and the analytical and simulation results presented here provide some guidance on determining maximum wind power penetration level given a frequency deviation limit.
Abstract: The integration of renewable energy sources into power systems has gathered significant momentum globally because of its unlimited supply and environmental benefits. Within the portfolio of renewable energy, wind power is expected to have a soaring growth rate in the coming years. Despite its well known benefits, wind power poses several challenges in grid integration. The inherent intermittent and non-dispatchable features of wind power not only inject additional fluctuations to the already variable nature of frequency deviation, they also decrease frequency stability by reducing the inertia and the regulation capability. This paper closely examines these effects as well as the effect on tie-line flows and area control error, which causes a larger and longer frequency deviation in the integrated system. Further, the effect of wind power on frequency regulation capability at different penetration levels is also examined. The analytical and simulation results presented here provide some guidance on determining maximum wind power penetration level given a frequency deviation limit.
TL;DR: In this paper, a tractable adaptive min-max-min cost model is introduced to find a robust optimal expansion plan for new lines and storages withstanding the worst-case realization of the uncertain variables.
Abstract: This paper presents a new nondeterministic model for joint transmission and energy storage expansion planning along with optimal transmission switching in wind farm-integrated power systems. The proposed approach adopts the underlying idea of robust optimization to characterize the uncertainty sources pertaining to load demands and wind power productions through uncertainty sets. Accordingly, a tractable adaptive min–max–min cost model is introduced to find a robust optimal expansion plan for new lines and storages withstanding the worst-case realization of the uncertain variables. As the adaptive min–max–min cost model cannot be solved directly by the commercial off-the-shelf software packages, a decomposition algorithm using primal cutting planes is introduced to obtain the optimal solution. The proposed approach has been implemented on the IEEE 24-bus and the IEEE 73-bus test systems. Also, the robustness of optimal expansion plans under different circumstances is evaluated through a post-optimization procedure simulating different realizations of the uncertainty sources. Case studies justify the efficiency of the proposed RO-based model.
TL;DR: In this paper, the authors present a computationally efficient unit commitment/maintenance/capacity planning formulation that includes the critical operating constraints, and show that the omission of flexibility can lead to a system that is unable to meet demand, carbon, and RPS requirements.
Abstract: Recent work on operational flexibility—a power system’s ability to respond to variations in demand and supply—has focused on the impact of large penetration of renewable generation on existing power systems. Operational flexibility is equally important for long-term capacity expansion planning. Future systems with larger shares of renewable generation, and/or carbon emission limits, will require flexible generation mixes; yet, flexibility is rarely fully considered in capacity planning models because of the computational demands of including mixed integer unit commitment within capacity expansion. We present a computationally efficient unit commitment/maintenance/capacity planning formulation that includes the critical operating constraints. An example of capacity planning for a Texas-like system in 2035 with hypothetical RPS and carbon policies shows how considering flexibility results in different capacity and energy mixes and emissions, and that the omission of flexibility can lead to a system that is unable to simultaneously meet demand, carbon, and RPS requirements.
TL;DR: In this paper, a model for the simultaneous allocation of capacitor banks and distributed generation, which takes into account the stochastic nature of distributed generation is presented, and an efficient hybrid method based on Tabu search and genetic algorithms is proposed to solve the problem.
Abstract: Optimal and simultaneous siting and sizing of distributed generators and capacitor banks in distribution systems have attracted a lot of attention from distribution companies. The placement and capacity of these devices have direct effects on the system’s performance. This paper presents a model for the simultaneous allocation of capacitor banks and distributed generation, which takes into account the stochastic nature of distributed generation. To solve the model presented, we propose an efficient hybrid method based on Tabu search and genetic algorithms. The hybrid method is applied to a well-known system in literature.
TL;DR: In this paper, a stochastic unit commitment approach with wind power forecast uncertainty and energy storage is proposed to evaluate the potential value of energy storage in power systems with renewable generation.
Abstract: The fast growing expansion of renewable energy increases the complexities in balancing generation and demand in the power system. The energy-shifting and fast-ramping capability of energy storage has led to increasing interests in batteries to facilitate the integration of renewable resources. In this paper, we present a two-step framework to evaluate the potential value of energy storage in power systems with renewable generation. First, we formulate a stochastic unit commitment approach with wind power forecast uncertainty and energy storage. Second, the solution from the stochastic unit commitment is used to derive a flexible schedule for energy storage in economic dispatch where the look-ahead horizon is limited. Analysis is conducted on the IEEE 24-bus system to demonstrate the benefits of battery storage in systems with renewable resources and the effectiveness of the proposed battery operation strategy.
TL;DR: A reformulation approach based on the potential function is proposed, which can transform the bi-level equilibrium model into an integrated single-level optimization problem to enhance the computation efficiency and indicate that the ESSs indirectly but substantially provide improved flexibilities while pursuing individual profit maximization.
Abstract: Energy storage systems (ESSs) are of great value to realize energy management and to support large-scale renewable generation. The combined operation of ESSs and renewables is one way to achieve output levelling and to improve the integration of sustainable energy. However, in a market-based environment, ESSs would make strategic decisions on self-schedules and arbitrage in energy and ancillary service markets, maximizing the overall profits. Will the strategic operation of ESSs promote renewable generation integration? To explicitly answer this question, this paper proposes a multi-period Nash-Cournot equilibrium model for joint energy and ancillary service markets to evaluate the contribution of the ESSs for supporting renewable generation. Then, a reformulation approach based on the potential function is proposed, which can transform the bi-level equilibrium model into an integrated single-level optimization problem to enhance the computation efficiency. Numerical examples are implemented to validate the effectiveness of the reformulation technique. The results of the case study indicate that the ESSs indirectly but substantially provide improved flexibilities while pursuing individual profit maximization.
TL;DR: In this article, an integrated stochastic day-ahead scheduling model is proposed to dispatch hourly generation and load resources and deploy flexible ramping for managing the variability of renewable energy system.
Abstract: This paper proposes an integrated stochastic day-ahead scheduling model to dispatch hourly generation and load resources and deploy flexible ramping for managing the variability of renewable energy system. A comprehensive framework for the natural gas transportation network is considered to address the dispatchability of a fleet of fuel-constrained natural gas-fired units. System uncertainties include the day-ahead load and renewable generation forecast errors. Illustrative examples demonstrate that the real-time natural gas delivery can directly impact the hourly dispatch, flexible ramp deployment, and power system operation cost. Meanwhile, the demand side participation can mitigate the dependency of electricity on natural gas by providing a viable option for flexible ramp when the natural gas system is constrained.
TL;DR: In this article, a novel planning method of fast electric vehicle (EV) charging stations on a round freeway was developed, considering the spatial and temporal transportation behaviors, which can accurately determine the most suitable locations for EV fast charging stations considering the travelling convenience of EV users, and also minimize the sum of waiting cost and charger cost.
Abstract: A novel planning method of fast electric vehicle (EV) charging stations on a round freeway was developed, considering the spatial and temporal transportation behaviors. A spatial and temporal model based on the origin-destination (OD) analysis was developed to obtain all the EV charging points (the location on the round freeway that an EV needs recharging due to the low battery capacity). Based on a shared nearest neighbor (SNN) clustering algorithm, a location determination model was developed to obtain the specific locations for EV charging stations with their service EV clusters. A capacity determination model based on the queuing theory was proposed to determine the capacity of each EV charging station. The round-island freeway in Hainan Island of China was employed as a test system to illustrate the planning method. Simulation results show that the developed planning method can not only accurately determine the most suitable locations for EV fast charging stations considering the travelling convenience of EV users, but also minimize the sum of waiting cost and charger cost.
TL;DR: In this paper, a distributed local control scheme for dc microgrid is proposed along with the basic droop control, where the line resistances cannot be neglected and a centralized controller in each area is used to make the tie-line power flow zero at steady state.
Abstract: In this paper, a distributed local control scheme for dc microgrid is proposed along with the basic droop control. It eliminates the limitations of droop control when the distributed generators are geographically distributed, for which, the line resistances cannot be neglected. Effects of line inductance and constant power loading (CPL) are investigated by analyzing the voltage tracking transfer function for single source system. Stability of two sources single load microgrid with proposed controller is investigated. Simulated responses are presented for two sources single load microgrid (for the sake of simplicity) to depict the proper load sharing and voltage improvement capability of the proposed control method with the consideration of line resistances. However, this can be extended to multiple-source multiple-load configuration connected to the dc bus. A comparison of the result is presented to show the better performance of the proposed control scheme as compared to the conventional droop control and hierarchical secondary control. The interconnected operation of the microgrid is also investigated to show the applicability of the proposed control in the interconnected mode. A centralized controller in each area is used to make the tie-line power flow zero at steady state.
TL;DR: A model to determine the optimal size of an energy storage facility from a strategic investor's perspective, which considers the uncertainties associated with rival generators' offering strategies and future load levels.
Abstract: This paper proposes a model to determine the optimal size of an energy storage facility from a strategic investor's perspective. This investor seeks to maximize its profit through making strategic planning, i.e., storage sizing, and strategic operational, i.e., offering and bidding, decisions. We consider the uncertainties associated with rival generators’ offering strategies and future load levels in the proposed model. The strategic investment decisions include the sizes of charging device, discharging device, and energy reservoir. The proposed model is a stochastic bi-level optimization problem; the planning and operation decisions are made in the upper-level, and market clearing is modeled in the lower-level under different operating scenarios. To make the proposed model computationally tractable, an iterative solution technique based on Benders’ decomposition is implemented. This provides a master problem and a set of subproblems for each scenario. Each subproblem is recast as an mathematical programs with equilibrium constraints. Numerical results based on real-life market data from Alberta's electricity market are provided.
TL;DR: A hybrid deterministicprobabilistic method where a temporally local moving window technique is used in Gaussian process (GP) to examine estimated forecasting errors and can substantially reduce the forecasting error while it is more likely to generate Gaussian-distributed residuals.
Abstract: The demand for sustainable development has resulted in a rapid growth in wind power worldwide. Although various approaches have been proposed to improve the accuracy and to overcome the uncertainties associated with traditional methods, the stochastic and variable nature of wind still remains the most challenging issue in accurately forecasting wind power. This paper presents a hybrid deterministicprobabilistic method where a temporally local moving window technique is used in Gaussian process (GP) to examine estimated forecasting errors. This temporally local GP employs less measurement data with faster and better predictions of wind power from two wind farms, one in the USA and the other in Ireland. Statistical analysis on the results shows that the method can substantially reduce the forecasting error while it is more likely to generate Gaussian-distributed residuals, particularly for short-term forecast horizons due to its capability to handle the time-varying characteristics of wind power.
TL;DR: This work proposes and experimentally validate a process to dispatch the operation of a distribution feeder with heterogeneous prosumers according to a trajectory with 5 min resolution, called dispatch plan, established the day before the operation.
Abstract: We propose and experimentally validate a process to dispatch the operation of a distribution feeder with heterogeneous prosumers according to a trajectory with 5 min resolution, called dispatch plan , established the day before the operation. The controllable element is a utility-scale grid-connected battery energy storage system (BESS) integrated with a minimally pervasive monitoring infrastructure. The process consists of two stages: day-ahead, where the dispatch plan is determined by using forecast of the aggregated consumption and local distributed generation (prosumption), and real-time operation, where the mismatch between the actual prosumption realization and dispatch plan is compensated for thanks to adjusting the real power injections of the BESS with model predictive control (MPC). MPC accounts for BESS operational constraints thank to reduced order dynamic grey-box models identified from online measurements. The experimental validation is performed by using a grid-connected 720 kVA/500 kWh BESS to dispatch the operation of a 20-kV distribution feeder of the Ecole Polytechnique Federale de Lausanne campus with both conventional consumption and distributed photo-voltaic generation.