Showing papers in "CSEE Journal of Power and Energy Systems in 2019"

Artificial intelligence based smart energy community management: A reinforcement learning approach

Abstract: This paper presents a smart energy community management approach which is capable of implementing P2P trading and managing household energy storage systems. A smart residential community concept is proposed consisting of domestic users and a local energy pool, in which users are free to trade with the local energy pool and enjoy cheap renewable energy while avoiding the installation of new energy generation equipment. The local energy pool could harvest surplus energy from users and renewable resources, at the same time it sells energy at a higher price than Feed-in-Tariff (FIT) but lower than the retail price. In order to encourage the participation in local energy trading, the electricity price of the energy pool is determined by a real-time demand/supply ratio. Under this pricing mechanism, retail price, users and renewable energy could all affect the electricity price which leads to higher consumers' profits and more optimized utilization of renewable energy. The proposed energy trading process was modeled as a Markov Decision Process (MDP) and a reinforcement learning algorithm was adopted to find the optimal decision in the MDP because of its excellent performance in on-going and model-free tasks. In addition, the fuzzy inference system makes it possible to use Q-learning in continuous state-space problems (Fuzzy Q-learning) considering the infinite possibilities in the energy trading process. To evaluate the performance of the proposed demand side management system, a numerical analysis is conducted in a community comparing the electricity costs before and after using the proposed energy management system.

Deep reinforcement learning for power system applications: An overview

Abstract: Due to increasing complexity, uncertainty and data dimensions in power systems, conventional methods often meet bottlenecks when attempting to solve decision and control problems. Therefore, data-driven methods toward solving such problems are being extensively studied. Deep reinforcement learning (DRL) is one of these data-driven methods and is regarded as real artificial intelligence (AI). DRL is a combination of deep learning (DL) and reinforcement learning (RL). This field of research has been applied to solve a wide range of complex sequential decision-making problems, including those in power systems. This paper firstly reviews the basic ideas, models, algorithms and techniques of DRL. Applications in power systems such as energy management, demand response, electricity market, operational control, and others are then considered. In addition, recent advances in DRL including the combination of RL with other classical methods, and the prospect and challenges of applications in power systems are also discussed.

A review on energy management, operation control and application methods for grid battery energy storage systems

Abstract: Energy storage is the key means to improving the flexibility, economy and security of the power system. It is also important in promoting new energy consumption and energy Internet. Therefore, energy storage expected to support distributed power and micro-grid, promote open sharing and flexible trading of energy production and consumption, and realize multi-functional coordination. In recent years, with the rapid development of the battery energy storage industry, its technology has shown the characteristics and trends for large-scale integration and distributed applications with multi-objective collaboration. As a grid-level application, energy management systems (EMS) of battery energy storage system (BESS) were deployed at utility control centers as an important component of power grid management in real-time. Based on the analysis of the development status of BESS, this paper introduced its application scenarios such as reduction of power output fluctuations, accordance to the output plan at renewable energy generation side, power grid frequency adjustment, power flow optimization at power transmission side, and distributed and mobile energy storage system at power distribution side. The studies and application status of BESS in recent years were reviewed. The energy management, operation control methods, and application scenes of large-scale BESS were also prospected in the study.

Overview of energy storage in renewable energy power fluctuation mitigation

Abstract: The integration of renewable energy, such as PV and wind power, has exerted great impacts on the power system with its rapid development. If the corresponding energy storage system is configured, the power system could be able to hold a higher proportion of renewable energy. Focusing on energy storage application for the output fluctuation mitigation of renewable energy, this paper first analyses the reason for renewable energy power fluctuation mitigation from the four aspects of frequency, unit ramp, low frequency oscillation and cascading failure. In addition, the fluctuation rate standard of grid-connected renewable energy, the energy storage type and the mitigation topology are introduced. Then a summary and analysis on mitigation strategy and hybrid energy storage allocation strategy are presented. Finally, the demonstration application and development trend of energy storage are analyzed to provide reference for the promotion of energy storage in renewable energy.

Optimal planning of AC-DC hybrid transmission and distributed energy resource system: Review and prospects

Abstract: With the highly-extensive integration of distributed renewable energy resources (DER) into the grid, the power distribution system has changed greatly in the structure, function and operating characteristics. On this ground, An AC-DC hybrid DER system becomes necessary for effective management and control over DER. This paper first summarizes the physical characteristics and morphological evolution of AC-DC hybrid DER system. The impact of these new features on system configuration planning is analyzed with respect to its flexible networking, rich operation control modes, and tight source-network-load-storage coupling. Then, based on a review of the existing research, problems and technical difficulties are figured out in terms of converter modeling, steady-state analysis, power flow calculation, operating scenarios management, and optimization model solution. In light of the problems and difficulties, a framework for the configuration optimization of AC-DC hybrid DER systems is proposed. At last, the paper provides a prospect of key technologies from six aspects including morphology forecasting, coupling interaction analysis, uncertainty modeling, operation simulation, optimization model solving algorithm and comprehensive scheme evaluation.

Integrated optimization of smart home appliances with cost-effective energy management system

Abstract: Smart grid enables consumers to control and schedule the consumption pattern of their appliances, minimize energy cost, peak-to-average ratio (PAR) and peak load demand. In this paper, a general architecture of home energy management system (HEMS) is developed in smart grid scenario with novel restricted and multi-restricted scheduling method for the residential customers. The optimization problem is developed under the time of use pricing (TOUP) scheme. To optimize the formulated problem, a powerful meta-heuristic algorithm called grey wolf optimizer (GWO) is utilized, which is compared with particle swarm optimization (PSO) algorithm to show its effectiveness. A rooftop photovoltaic (PV) system is integrated with the system to show the cost effectiveness of the appliances. For analysis, eight different cases are considered under various time scheduling algorithms.

Digital twin framework and its application to power grid online analysis

Abstract: Digital twin (DT) framework is introduced in the context of application for power grid online analysis. In the development process of a new power grid real-time online analysis system, an online analysis digital twin (OADT) has been implemented to realize the new online analysis architecture. The OADT approach is presented and its prominent features are discussed. The presentation, discussion, and performance testing are based on a large-scale grid network model (40K+ buses), exported directly from the EMS system of an actual power grid. A plan to apply the OADT approach to digitize power grid dispatching rules is also outlined.

Key technologies of ultra-high voltage hybrid LCC-VSC MTDC systems

Abstract: The key technologies of ultra-high voltage hybrid LCC-VSC MTDC systems are investigated, focusing on the design of system configurations, converter topologies and the control and protection system. A double converter per pole of VSC connection is proposed along with the design of a 5000 MW VSC valve to develop a ± 800 kV/5000 MW large-capacity power transmission. The hybrid MMC topology capable of clearing the DC faults and the control strategy are developed to effectively improve the reliability in case of overhead line faults. The control and protection system of the LCC-VSC MTDC system is introduced to offer flexible operations under both normal and abnormal conditions, which includes voltage/current margin-based coordination, converter switch-in and switch-out, re-connection and drop-off of a third station. Simulations of an LCC-VSC MTDC system based on the LCC-VSC MTDC project are performed.

Review of system strength and inertia requirements for the national electricity market of Australia

Abstract: Synchronous generators (SGs) are still making major contributions to the re-stabilization of a power system following voltage/frequency disturbances, attributed to their inherent capability of providing system strength and inertia. However, SGs powered by fossil fuels are operating to a lesser extent and scheduled for decommissioning in the National Electricity Market (NEM) of Australia due to the accelerating increase of low bidding priced asynchronous generation of wind and solar, which leads to the reduction and even in some cases, a shortage of system strength and inertia. This paper comprehensively reviews the requirements of system strength and inertia in the NEM from an operational security perspective. Australia is the first country that established the regulation rules of system strength and inertia to accommodate issues of an emerging high penetration level of non-synchronous renewable generation.

A multi energy storage system model based on electricity heat and hydrogen coordinated optimization for power grid flexibility

Abstract: Based on decreasing the flexibility of the power grid through the integration of large-scale renewable energy, a multi-energy storage system architectural model and its coordination operational strategy with the same flexibility as in the pumped storage power station and battery energy storage system (BESS) are studied. According to the new energy fluctuation characteristics and the different peak valley parameters in the power grid, this paper proposes a electricity heat hydrogen multienergy storage system (EHH-MESS) and its coordination and optimization operational model to reduce the curtailment of wind power and photovoltaic (PV) to the power grid and improve the flexibility of the power grid. Finally, this paper studied the simulation model of an energy storage optimization control strategy after the multi-energy storage system is connected to the distribution networks, and analyzed three operational modes of the multi-energy storage system. The simulation results show that the EHH-MESS proposed in this paper has a better power grid regulation flexibility and economy, and can be used to replace the battery energy storage system based on MATLAB.

Evaluating the vulnerability of integrated electricity-heat-gas systems based on the high-dimensional random matrix theory

Abstract: Faced with the tight coupling of multi energy sources, the interaction between different energy supply systems makes it difficult for integrated energy systems (IES) to identify weak nodes. Based on the analysis of the data generated by the actual operation of IES, this paper proposes a weak node identification method based on random matrix theory (RMT). First, establish a unified power flow model for IES. Secondly. introduce RMT and the characteristics of weak nodes, without considering the detailed physical model of the system, using historical data and real-time data to construct the random matrix. Thirdly, the two limit spectrum distribution functions (Marchenko-Pastur law and ring law) are used to qualitatively analyze the system's operating status, calculate linear eigenvalue statistics such as mean spectral radius (MSR), and establish the weak node identification model based on entropy theory. Finally, the simulation of IES verifies the effectiveness of the proposed method and provides a new approach for the identification of weak nodes in IES.

Very short-term spatial and temporal wind power forecasting: A deep learning approach

Abstract: In power systems that experience high penetration of wind power generation, very short-term wind power forecast is an important prerequisite for look-ahead power dispatch. Conventional univariate wind power forecasting methods at presentonly utilize individual wind farm historical data. However, studies have shown that forecasting accuracy canbe improved by exploring both spatial and temporal correlations among adjacent wind farms. Current research on spatial-temporal wind power forecasting is based on relatively shallow time series models that, to date, have demonstrated unsatisfactory performance. In this paper, a convolution operation is used to capture the spatial and temporal correlations among multiple wind farms. A novel convolution-based spatial-temporal wind power predictor (CSTWPP) is developed. Due to CSTWPP's high nonlinearity and deep architecture, wind power variation features and regularities included in the historical data can be more effectively extracted. Furthermore, the online training of CSTWPP enables incremental learning, which makes CSTWPP non-stationary and in conformity with real scenarios. Graphics processing units (GPU) is used to speed up the training process, validating the developed CSTWPP for real-time application. Case studies on 28 adjacent wind farms are conducted to show that the proposed model can achieve superior performance on 5-30 minutes ahead wind power forecasts.

Quasi-dynamic interactions and security control of integrated electricity and heating systems in normal operations

Abstract: Coupling between electricity systems and heating systems are becoming stronger, leading to more flexible and more complex interactions between these systems. The operation of integrated energy systems is greatly affected, especially when security is concerned. Steady-state analysis methods have been widely studied in recent research, which is far from enough when the slow thermal dynamics of heating networks are introduced. Therefore, an integrated quasi-dynamic model of integrated electricity and heating systems is developed. The model combines a heating network dynamic thermal model and the sequential steady-state models of electricity networks, coupling components, and heating network hydraulics. Based on this model, a simulation method is proposed and quasi-dynamic interactions between electricity systems and heating systems are quantified with the highlights of transport delay. Then the quasi-dynamic interactions were applied using security control to relieve congestion in electricity systems. Results show that both the transport delay and control strategies have significant influences on the quasi-dynamic interactions.

Reliability assessment of a cyber physical microgrid system in island mode

Abstract: As cyber physical systems, microgrids (MGs), with distributed generations and energy management systems, can improve the reliability of power supply for customers in MGs. To quantify the reliability of isolated MGs, a cyber-physical assessment model is proposed. In this model, the circuit breakers and distributed energy resources are treated as the coupling elements between the cyber system and physical system, where the circuit breakers are uniquely modelled by using the Markov process theory based on the indirect interdependencies between cyber physical elements. For the cyber system, the reliability model of communication networks is formulated based on the link connectivity evaluation method. For the physical system, a system state generating method is presented to account for the optimal operation strategy, which considers the influence of the optimization strategy on the failure consequence analysis. With the proposed cyber and physical reliability models, the sequential Monte Carlo (SMC) simulation method is adopted to assess the reliability of islanded MGs. Simulations are carried out on a test system, and results verify the feasibility and effectiveness of proposed assessment method. Furthermore, one application of the proposed method is on the parameter setting of the cyber system, in terms of enhancing MGs reliability.

Overview of mechanism and mitigation measures on multi-frequency oscillation caused by large-scale integration of wind power

Abstract: In recent years, the large-scale integration of renewable energy sources represented by wind power and the widespread application of power electronic devices in power systems have led to the emergence of multi-frequency oscillation problems covering multiple frequency segments, which seriously threaten system stability and restrict the accommodation of renewable energy. The oscillation problems related to renewable energy integration have become one of the most popular topics in the field of wind power integration and power system stability research. It has received extensive attention from both academia and industries with many promising research results achieved to date. This paper first analyzes several typical multi-frequency oscillation events caused by large-scale wind power integration in domestic and foreign projects, then studies the multi-frequency oscillation problems, including wind turbine's shafting torsional oscillation, sub/super-synchronous oscillation and high frequency resonance. The state of the art is systematically summarized from the aspects of oscillation mechanism, analysis methods and mitigation measures, and the future research directions are explored.

Impact of synchronous condenser on the dynamic behavior of LCC-based UHVDC system hierarchically connected to AC system

Abstract: Hierarchical connection (HC) is a very attractive mode for ± 800 kV line commutated converter based ultra high voltage direct current (LCC-UHVDC) system connected to different AC voltage levels because of its ability to reduce the scale factor of a converter transformer. Faults in the HC-UHVDC system can cause commutation failure (CF). In this paper, impact of synchronous condenser (SC) to mitigate CF in HC-UHVDC system is analyzed. A ± 800KV HC-UHVDC system along with synchronous condenser is built in PSCAD/EMTDC. Transient performance analysis of HC-UHVDC for single and three phase to ground faults is investigated. Commutation failure immunity index (CFII), commutation failure probability index (CFPI), fault recovery time (FRT), and transient overvoltage (TOV) are used as measures to evaluate the effects of SC at HC-UHVDC system design. The simulation results show that SC can make the HCUHVDC system less susceptible to CF, effectively improve fault recovery performances of the overall system, and reduce transient overvoltage when single or multiple converters are blocked. The results of this research can provide technical assistance in real world HC-UHVDC projects.

An optimal dispatch strategy for distributed microgrids using PSO

Abstract: Grid structures are rapidly evolving in view of contemporary energy policies which ensure the addition of more renewable sources to reduce the carbon footprint. Compared to a centralized approach, low voltage grids (decentralized and distributed) are promising approaches to integrating non-dispatchable renewable energy sources (RESs). Installing local micro level power generation sources such as fuel cells, microturbines, and energy storage systems are a recent trend which helps in the intermittent effects of RESs and makes microgrids less dependable on the main grid. Due to the increasing variety of distributed generation sources having diverse characteristics, power dispatch scheduling of distributed microgrids is becoming challenging. A dispatch scheduling solution from an operator's point of view is presented by the authors. The core objective of this study is to minimize the carbon emissions and the cost of each microgrid. Further, it is observed that sales and purchases from the main grid are reduced. Consequently, transmission losses are also decreased.

Single-phase-to-ground fault detection with distributed parameters analysis in non-direct grounded systems

Abstract: The fast and accurate detection of the single-phase-to-ground fault is of great significance for the reliability and safety of the power supply. In this paper, novel algorithms for distribution network protection were proposed with distributed parameters analysis in non-direct grounded systems. At first, novel generating mechanisms of zero-sequence voltage and residual current were proposed. Then the compositions of residue parameters, including residual current and residual admittances, were decomposed in detail. After that, an improved algorithm for a fault resistance calculation of a single phase-to-earth fault was also proposed, and the algorithm is much more convenient as it only needs to measure the variation of the zero-sequence voltage and does not need the prerequisites of the faulty feeder selection. Furthermore, the fault feeder can also be selected by an improved calculation algorithm of zero-sequence admittance of the faulty feeder, which cannot be affected by the asymmetry of the network. Theoretical analysis and the MATLAB/Simulink simulation results demonstrate the effectiveness of the proposed algorithms.

Review of energy management and planning of islanded microgrids

Abstract: This survey paper provides a critical overview of optimization formulations for planning and operation of islanded microgrids, including optimization objectives, constraints, and control variables. The optimization approaches reviewed address methods both for increasing the resiliency of advanced distribution systems and electrification of remote communities. This paper examines over 120 individual optimization studies and discovers that all optimizations studies of islanded microgrids are based on formulations selecting a combination of 16 possible objective functions, 14 constraints, and 13 control variables. Each of the objectives, constraints, and variables are discussed exhaustively both from the perspective of their importance to islanded microgrids and chronological trends in their popularity.

ADMM-based distributed state estimation for integrated energy system

Abstract: In order to have an accurate knowledge of systemwide operation states, it is necessary to perform state estimation for the integrated energy system (IES) as the basis of energy management and control. Centralized state estimation is practically infeasible for IES due to the unreliability of communication, the barrier on privacy, and the large scale of integrated systems. This paper proposes a distributed state estimation algorithm based on the alternating direction method of multipliers (ADMM) for IES containing electricity, heat, and natural gas. Various coupling units are taken into full consideration in modeling of IES state estimation to reflect the harmonization of multi energy. On the basis of bilinear measurement model, the state estimation considering nonlinear measurements can be replaced by an equivalent three-stage problem containing two linear state estimations and an intermediate transformation to avoid nonconvex optimization. The three-stage procedure for IES state estimation can be further decoupled over three sub-systems with coordination on coupling units, yielding a fully distributed scheme based on ADMM. A modified ADMM with the self-adjusting penalty parameter is also adopted to enhance the convergence. Simulation results demonstrate the validity and superiority of the proposed algorithm.

The reliability and operational test system of a power grid with large-scale renewable integration

Abstract: This paper proposes a reliability and operational test system named XJTU-ROTS2017, characterized by large-scale renewable power integration and long-distance transmission. The test system has 38 nodes, 63 lines, 15 transformers and 20 generators in three areas, with peak load 10,421 MW and total installed capacity 16050 MW. Electricity primarily transmits from a resource-rich area to a load area, carrying wind/solar power generation. The determination of component parameters and grid topology is based on design manuals and typical practices. The test system can be conveniently applied to reliability evaluation and operation optimization of composite power systems integrating coal/hydro/solar/wind resources. Finally, the extended applications to AC/DC hybrid power systems and interconnected power systems are discussed.

Scenario-based planning of active distribution systems under uncertainties of renewable generation and electricity demand

Abstract: The rising penetration of intermittent renewable distributed generation leads to uncertainties in the planning of electric distribution networks. Fully considering the uncertainties pertinent to wind power generation, photovoltaic power generation and load demand, this paper proposes a scenario-based model for the planning of active distribution systems. The solution obtains the optimal capacities and locations of wind and photovoltaic based distributed generators in the distribution system, whilst minimizing the active and reactive power losses as well as voltage deviation. A scenario matrix is generated using the heuristic moment matching technique that captures the stochastic moments and correlation among historical wind and photovoltaic power, and electricity demand. The scenario matrix is then incorporated to propose a stochastic planning model that considers a multi-objective index for minimizing power losses and voltage deviation. Finally, the effectiveness of the proposed planning model is confirmed using case-studies in 53-bus and IEEE 123-bus distribution systems.

Optimal operation of integrated energy systems subject to coupled demand constraints of electricity and natural gas

Abstract: This paper proposes a hybrid multi-objective optimization and game-theoretic approach (HMOGTA) to achieve the optimal operation of integrated energy systems (IESs) consisting of electricity and natural gas (E&G) utility networks, multiple distributed energy stations (DESs), and multiple energy users (EUs). The HMOGTA aims to solve the coordinated operation strategy of the electricity and natural gas networks considering the demand characteristics of DESs and EUs. In the HMOGTA, a hierarchical Stackelberg game model is developed for generating equilibrium strategies of DESs and EUs in each district energy network (DEN). Based on the game results, we obtain the coupling demand constraints of electricity and natural gas (CD-CENs) which reflect the relationship between the amounts and prices of electricity and cooling (E&C) that DESs purchase from utility networks. Furthermore, the minimization of conflicting costs of E&G networks considering the CDCENs are solved by a multi-objective optimization method. A case study is conducted on a test IES composed of a 20-node natural gas network, a modified IEEE 30-bus system, and 3 DENs, which verifies the effectiveness of the proposed HMOGTA to realize fair treatment for all participants in the IES.

A day-ahead economic dispatch method considering extreme scenarios based on wind power uncertainty

Abstract: As the intermittency of wind power is a growing concern in the day-ahead economic dispatch, this paper proposes a day-ahead economic dispatch method considering extreme scenarios of wind power by using an uncertainty set. The uncertainty set inspired by robust optimization is used to describe wind power intermittency in this paper. Four extreme scenarios based on the uncertainty set are formulated to represent the worst cases of wind power fluctuation. An economic dispatch method considering the costs of both load shedding and wind curtailment is proposed. The economic dispatch model can be easily solved by a quadratic programming method owing to the introduction of four extreme scenarios and the uncertainty set of wind power. Simulation is done using the IEEE 30-bus system and the results verify the effectiveness of the proposed method.

A model of electro-thermal hybrid energy storage system for autonomous control capability enhancement of multi-energy microgrid

Abstract: In view of the problem of low self-service capability of the microgrid due to the high operating cost and low capacity of the traditional battery energy storage system. In this paper, an electrothermal hybrid energy storage model based on electricity, hydrogen and thermal energy conversion and storage is introduced, and a microgrid autonomous operational strategy is proposed. First, the addition of the power to hydrogen transfer equipment in the traditional combined heat and power (TCHP) system without battery energy storage is studied, and a micro gas turbine, electric to hydrogen transfer equipment and electric boiler based electrothermal energy storage system (ETSS) model is established. Aiming at the lowest comprehensive operating cost of multiple energy sources in a microgrid and maximizing the consumption of curtailed wind, the multiobjective scheduling model of an electrothermal hybrid energy storage system is established, then the multi-energy autonomous operational strategy of a microgrid is proposed. Lastly, the simulation of a multi-energy microgrid in Northeast China is taken as an example. The results of the simulation showed that compared with a combined heat and power microgrid system considering conventiona battery energy storage, a multi-energy microgrid system using electrothermal hybrid energy storage has better flexibility and economy, and can improve wind power accommodation.

Stochastic dynamic economic dispatch of wind-integrated electricity and natural gas systems considering security risk constraints

Abstract: As the proportion of wind power generation increases in power systems, it is necessary to develop new ways for wind power accommodation and improve the existing power dispatch model. The power-to-gas technology, which offers a new approach to accommodate surplus wind power, is an excellent way to solve the former. Hence, this paper proposes to involve power-to-gas technology in the integrated electricity and natural gas systems (IEGSs). To solve the latter, on one hand, a new indicator, the scale factor of wind power integration, is introduced into the wind power stochastic model to better describe the uncertainty of grid-connected wind power; on the other hand, for quantizing and minimizing the impact of the uncertainties of wind power and system loads on system security, security risk constraints are established for the IEGS by the conditional value-at-risk method. By considering these two aspects, an MILP formulation of a security-risk based stochastic dynamic economic dispatch model for an IEGS is established, and GUROBI obtained from GAMS is used for the solution. Case studies are conducted on an IEGS consisting of a modified IEEE 39-bus system and the Belgium 20-node natural gas system to examine the effectiveness of the proposed dispatch model.

A combined reinforcement learning and sliding mode control scheme for grid integration of a PV system

Abstract: The paper presents development of a reinforcement learning (RL) and sliding mode control (SMC) algorithm for a 3-phase PV system integrated to a grid. The PV system is integrated to grid through a voltage source inverter (VSI), in which PV-VSI combination supplies active power and compensates reactive power of the local non-linear load connected to the point of common coupling (PCC). For extraction of maximum power from the PV panel, we develop a RL based maximum power point tracking (MPPT) algorithm. The instantaneous power theory (IPT) is adopted for generation reference inverter current (RIC). An SMC algorithm has been developed for injecting current to the local non-linear load at a reference value. The RL-SMC scheme is implemented in both simulation using MATLAB/SIMULINK software and on a prototype PV experimental. The performance of the proposed RL-SMC scheme is compared with that of fuzzy logic-sliding mode control (FL-SMC) and incremental conductance-sliding mode control (IC-SMC) algorithms. From the obtained results, it is observed that the proposed RL-SMC scheme provides better maximum power extraction and active power control than the FL-SMC and IC-SMC schemes.

Research and practice of flexibility in distribution systems: A review

Abstract: Flexibility in energy systems can support the operation of the electricity grid by providing active and reactive power to avoid voltage limit violations or congestion. Active distribution networks can provide this flexibility by implementing systems to control distributed generators, storage or loads. Additionally, power flow controlling devices can be used to implement operational flexibility in the energy system. This paper presents concepts for planning and forecasting of flexibility, monitoring of energy systems and control of flexibility from active distribution networks (ADNs) to enable the use of flexibility in future power systems.

Multi-objective dynamic optimal power flow of wind integrated power systems considering demand response

Abstract: This paper studies the economic environmental energy-saving day-ahead scheduling problem of power systems considering wind generation (WG) and demand response (DR) by means of multi-objective dynamic optimal power flow (MDOPF). Within the model, fuel cost, carbon emission and active power losses are taken as objectives, and an integrated dispatch mode of conventional coal-fired generation, WG and DR is utilized. The corresponding solution process to the MDOPF is based on a hybrid of a non-dominated sorting genetic algorithm-II (NSGA-II) and fuzzy satisfaction-maximizing method, where NSGA-II obtains the Pareto frontier and the fuzzy satisfaction-maximizing method is the chosen strategy. Illustrative cases of different scenarios are performed based on an IEEE 6-units\30-nodes system, to verify the proposed model and the solution process, as well as the benefits obtained by the DR into power system.

Benefit evaluation of the deep peak-regulation market in the northeast China grid

Abstract: In the Northeast China Grid (NCG), the percentage of wind power has reached nearly 20% of the total installed generation capacity, which causes increasing demands for deep peak-regulation capacity (DPC) during the operation of power systems. The shortage of DPC has become a significant problem in the NCG which may lead to wind curtailments and affect the security of power systems as well as the heating needs for inhabitants. In order to cope with this DPC shortage issue, the deep peak-regulation market (DPM) was established and has been running steadily in the past few years in NCG. This paper elaborates on the roles of the market players and the operational processes of the DPM, of which the advancements in terms of management mechanism are summarized. Moreover, benefits of the DPM for social harmony, environmental protection and economic efficiency are analyzed, for which relevant evaluation indices are proposed. A five-unit simulation system is constructed to illustrate the operation and benefits of the DPM. And focusing on comparisons with the previous Two Rules, the case study of Liaoning Power Grid verifies further that the DPM is feasible and able to bring more benefits to grids.