Search

To cope with the uncertainty of electric vehicles(EVs) and renewable energy in VPP, this paper proposes a two-stage robust optimal scheduling method for VPP considering the controllability of electric vehicle group (EVG). Highlights of the work in this paper are presented below: • The study found that the controlled capacity of EVG varies significantly, and the resulting operational benefits obtained by VPP vary significantly. It is recommended that operators should minimise the management of EVG in Category III status and increase the dispatch of EVs in Category I and II status in order to improve the controlled capacity of EVG and obtain more operational benefits. • Increasing the ESS capacity within a certain range can increase the revenue of the VPP, but the optimal capacity of the ESS should be configured is not the same when the controlled capacity of EVG varies. Further analysis reveals that when the total number of EVs and the nominal parameters of the system are constant, the VPP system shown in the paper can find the optimal ESS capacity for obtaining stable gains regardless of the number of EVs of each type. • When the forecast deviation coefficient is large, the day-ahead dispatching scheme for VPP developed by the two-stage robust optimization method has the highest total benefit and the strongest ability to resist fluctuations in WPP and PV output compared to other methods, with obvious advantages. To cope with the uncertainty of electric vehicles and renewable energy in virtual power plant(VPP), this paper proposes a two-stage robust optimal scheduling method for VPP considering the controllability of electric vehicle group (EVG). Firstly, the calculation method of the controlled capacity of EVG is given based on Minkowski's addition idea. Then a two-stage robust optimization model of min-max-min structure considering the controlled capability of EVG is developed, which involves the operational constraints and coordinated control of EVG, energy storage systems, convention gas turbine units, and distributed power sources. Finally, the model is solved based on the column and constraint generation(C&CG) algorithm. The results show that fully exploiting the controlled capabilities of EVG can help VPP optimize scheduling schemes and improve the economics of operation. [ABSTRACT FROM AUTHOR]

• Introduce a methodology to determine and deliver optimal flexibility to the distribution network. • Maximize social welfare within the local flexibility market. • Integrate the innovative concept of the value of flexibility loss in the local flexibility market. • Propose a linear programming model to optimize flexibility in local flexibility markets. • Enable flexibility exchange (import and export) with the Transmission System Operator. The increasing integration of Renewable Energy Sources (RESs) poses challenges due to their unpredictable nature. To address these challenges, flexible Distributed Energy Resources (DERs), such as Distributed Generators (DGs), Flexible Loads (FLs), and Energy Storage Systems (ESSs), have emerged in distribution networks. This paper proposes a linear programming formulation for optimizing flexibility provision in the Local Flexibility Market (LFM) to maximize Social Welfare (SW), contrasting with existing literature. The concept of the Value of Flexibility Loss (VOFL) is introduced to enable the Distribution System Operator (DSO) to optimize flexibility procurement, ensuring economic profitability for both providers and consumers. Additionally, the DSO can interchange flexibility with the Transmission System Operator (TSO) based on economic incentives. Numerical results demonstrate the accuracy and efficiency of the proposed model. [ABSTRACT FROM AUTHOR]

Electric demand and renewable power are highly variable, and the solution of a planning model relies on capturing this variability. This paper proposes a hybrid multi-area method that effectively captures both the intraday and interday chronology of real data considering extreme values, using a limited number of representative days, and time points within each day. An optimization-based representative extraction method is proposed to improve intraday chronology capturing. It ensures higher precision in preserving data chronology and extreme values than hierarchical clustering methods. The proposed method is based on a piecewise linear demand and supply representation, which reduces approximation errors compared to the traditional piecewise constant formulation. Additionally, sequentially linked day blocks with identical representatives, created through a mapping process, are employed for interday chronology capturing. To evaluate the efficiency of the proposed method, a comprehensive expansion co-planning model is developed, including transmission lines, energy storage systems, and wind farms. • Transmission and generation expansion problems are often based on time series. • Time series aggregation methods are used to reduce computational complexity. • We propose to use representative days with adaptively selected representative hours. • The efficiency is improved by using a piecewise linear dispatch representation. • Long-duration energy storages are modeled by tracking the daily state of charge. [ABSTRACT FROM AUTHOR]

• A comprehensive operational cost function was established based on the deep peak shaving characteristics of thermal generators. • The impacts of different combinations of renewable energy penetration on the power system operation were analyzed. • The effect of energy storage deployment on improving flexibility in high penetration renewable energy systems were analyzed. Higher proportions of renewable energy are one of the most prominent features of future power systems. However, renewable energy, such as wind and PV power generation, is subject to inherent variability and uncertainty, which leads to fluctuations in electrical production and necessities flexibility enhancement to ensure sufficient adjustable resources responding to such fluctuations. This paper proposes to enhance the flexibility of renewable-penetrated power systems by coordinating energy storage deployment and deep peak regulation of existing thermal generators. First, the growing flexibility requirement in the presence of variable renewable energy is discussed and quantified using proposed indices. Then, we analyze the characteristics of thermal generators providing deep peak regulation, and establish a comprehensive operation cost function including coal consumption, wear-and-tear consumption, and oil consumption of thermal generators. On this basis, we propose a flexibility enhancement method coordinating battery energy storage capacity optimization and deep peak regulation of thermal generators, which aims at minimizing the total investment and operation costs while satisfying operating constraints on representative days. Extensive case studies on a modified IEEE-RST 24 system verified the proposed flexibility enhancement method under different scenarios. [ABSTRACT FROM AUTHOR]

• An accurate power flow is formulated by utilizing a dynamic approximation. • A robust approach is used to model uncertain parameters, including RESs, loads, and market price. • A mixed-integer linear programming approach is provided for taking the powerful solvers into account. • The proposed linear framework is faster than the previous methods with less error. Decarbonization legislated the risen integration of renewable energy sources (RESs) in the presence of energy storage systems (ESS) in distribution networks. Nonetheless, these resources may impose substantial costs to the utilities for managing frequency. In the present era, the concept of active distribution networks (ADNs) has been taken into consideration in the study of grid penetration of RESs and ESSs. Accordingly, ADN requires a sophisticated AC optimal power flow (OPF) to solve such a complex decision-making process. This paper aims to develop an accurate and effective OPF in ADNs. To this end, this paper contributes to developing a highly efficient tool to operate ADNs under uncertainties in load and renewables. Firstly, a highly accurate and dynamic linear load flow is presented to compensate for the restrictions of nonlinear OPF, such as scalability and robustness. In more detail, the floating tangent surface method is utilized to approximate better the nonlinear and nonconvex equations in the OPF formula. For the iterative process of the dynamic linear approximation, its local convergence can be derived. Then, to make the model much more realistic, the uncertainty of RESs, load, and price is accounted for in a robust approach which alleviates their vulnerability in decisions. The proposed approach is finally formulated as a mixed integer linear problem and implemented in various case studies. After assessing the proposed strategy under different circumstances, the average error of different case studies is 3.30E-06 and 6.24E-02, respectively, for the proposed method and another approach in the literature, which means the proposed method has less error than other methods. From the results, it can be observed that the presented OPF solving method is vastly superior to other prior approaches in terms of more accurate results. [ABSTRACT FROM AUTHOR]

The increasing applications of renewable energy resources and energy storage units (ESU) raise the necessity for reliable, efficient, and economical power systems. However, the charge/discharging power loss of ESUs cannot be ignored in the islanded microgrid. This paper focuses on the economic dispatch (ED) problem of ESUs in DC microgrids to reduce charging/discharging power loss, meet the power balance requirement, and against communication delays. Specifically, the distributed cooperative control algorithm designed in this paper forces incremental costs of all ESUs to a consistent value to minimize the sum operation loss of all ESUs in the small time scale. Meanwhile, power imbalance auxiliary variables are added to the control algorithm to satisfy the power balance constraint in the control process. Moreover, the ubiquitous time delay effect of communication links on the dispatch performance is solved by presenting an aperiodic sampled data-driven control method. The control parameters are determined by solving strict linear matrix inequalities, and the system stability analysis is also presented through the Lyapunov–Krasovskii functionals. Compared with the traditional control methods, the provided algorithm can overcome the negative effect of communication delay and save 16.94% of the sum ESUs operation loss under the given scenario. • An economic dispatch optimization algorithm for energy storage units is proposed. • The power balance constraint can be satisfied during the control process. • Develop a buffer-based waiting time strategy to avoid the effect of time delay. • The stability of the presented closed-loop system is proved. [ABSTRACT FROM AUTHOR]

Battery energy storage systems (BESSs), while at the moment still expensive, are from a technical point of view exceptionally well suited to support a distribution system operator (DSO) in the challenges created by increasing distributed, fluctuating and uncertain generation from renewable energy sources (RES), as well as by the unbundling of electricity retailing and grid operation. This paper presents three grid applications and measurement results from the grid-connected Zurich 1 MW BESS. Specifically, provision of frequency reserves, peak shaving and islanded operation of a microgrid are discussed in detail. The battery unit is able to perform these diverse tasks with outstanding quality. For the provision of frequency reserves the system has passed all prequalification requirements posed by the transmission system operator (TSO) in order to be granted access to the ancilliary services market. Along with these applications, the paper aims to communicate the experience gained in designing proper control frameworks, communication infrastructure and the knowledge acquired concerning BESSs behavior. [ABSTRACT FROM AUTHOR]

Residential communities are increasingly adopting renewable energy sources (RES) to minimize energy consumption costs. However, these RES are weather-dependent and uncertain, posing challenges to ensuring reliable operations. Addressing the uncertainties in power supply management becomes a critical research question. Energy storage systems play a crucial role in providing battery-powered supply for residential loads under uncertain conditions. The operation of microgrids is directly influenced by uncertainties. This paper proposes data-driven-based net load uncertainty quantification fusion mechanisms for cloud-based energy storage management with renewable energy integration. Firstly, a fusion model is developed using SVR, LSTM, and CNN-GRU algorithms to estimate day-ahead load and PV power forecasting errors. After that, two mechanisms are proposed to determine the day-ahead net load error. In the first mechanism, the net load error is directly forecasted, while in the second mechanism, it is derived from the forecast errors of load and PV power. The net error analysis is conducted with a statistical mean and standard deviation, resulting in different uncertainty-bound confidence intervals around the forecasted value. Subsequently, the cloud energy storage system operation cost is calculated with the best uncertainty quantification mechanism for two different case studies. This approach allows for better management of uncertainties in energy storage systems and enables more informed decision-making under varying conditions. • Learning-based fusion algorithm for net load forecasting. • Develop uncertainty mechanism for uncertainty quantification. • Economic assessment of cloud energy storage. [ABSTRACT FROM AUTHOR]

• Application of mobile energy storage for distribution system operation is presented and operational difficulties in terms of constraint violations are identified. • A hybrid mobile and static energy storage system planning algorithm is proposed which overcomes the operational difficulties of mobile-only energy storage system. • Two objectives, operation cost and cost of lost load are considered. • Problem is modelled as mixed integer non-linear optimization model and is solved using multi-objective particle swarm optimization. • Comparison of costs related to static, mobile and static and mobile only cases are presented to show of advantage of having both static and mobile energy storage system. By utilizing their spatial flexibility, mobile energy storage systems provide various operation and restoration services for distribution systems. However, in distribution systems with high renewable energy resources penetration, the application of mobile energy storage systems for distribution system operations can jeopardize a few of the advantages of energy storage systems like power variability management, peak demand reduction, ramp rate control, and/or voltage regulation while utilizing their mobile functionality. Hence, to meet operational constraints in distribution systems with mobile energy storage systems, a minimum capacity of static energy storage systems is required. In this paper, an optimization framework is proposed for sizing and siting of a static and a mobile energy storage system. The main objective of the proposed framework is to maximize the distribution system operators profit while minimizing the excepted maximum cost of lost load in the event of external/internal outages. This is achieved by incorporating both mobile and static storage systems. The problem is formulated as a two-level multi-objective optimization model and solved using multi-objective particle swarm optimization. A significant reduction in ramp rate, peak demand, reverse power flow, and energy loss is achieved compared with the mobile energy storage system only case study. [ABSTRACT FROM AUTHOR]

Since it is expected to act on short timescales, fast frequency support FFS of energy storage systems is an efficient way to mitigate the effect of frequency deviation induced by renewable energy source fluctuations and reduced inertia in inverter-based microgrids. Therefore, accurate and fast frequency measurement is required to accomplish the FFS function for distributed generation based on converters. However, the state-of-the-art methods are based on the phase-locked-loop PLL which can induce instability in weak and defective grids, at the same time, FFS interventions are necessary in the grid's worst-case situations. Hence, this paper proposes a novel method for frequency and rate of change of frequency (ROCOF) estimation based on maximum likelihood and applied to grid-connected converters. The proposed approach operates in short time windows and off-nominal conditions and is compared to the commonly used techniques, the PLL, and the Discrete Fourier Transform DFT using various steady state and dynamic tests. Findings reveal better performance from the new technique in terms of accuracy and latency in multiple cases. • Fast and accurate frequency estimation crucial for Fast Frequency support. • Trade-off between accuracy and response time in frequency/ROCOF estimation. • Maximum Likelihood with short windows for precise frequency estimation. • Maximum Likelihood outperforms PLL and DFT in frequency estimation. [ABSTRACT FROM AUTHOR]

• A novel distributed event-triggered control system for DC microgrids with the fractional-order local controllers, for voltage stabilization and balancing of ESSs has been proposed in this paper. The controllers communicate in an event-based manner over a sparse communication graph to achieve an average consensus of the bus voltages and energy levels of ESSs. Furthermore, the FOPI controllers are tuned in the primary layer that further increases the performance of the droop controller for accurate power-sharing. The paper's contributions can therefore be outlined as: • A distributed event-triggered secondary control system for DC microgrids to achieve voltage stabilization and balanced ESS levels. • FOPI local droop controllers for accurate power-sharing and higher disturbance rejection caused by the intermittent sources. Recently, direct current (DC) microgrids have become prominent due to their less complicated architecture and better integration of renewable energy sources (RES). Energy storage systems (ESSs) and RES, such as photovoltaic (PV) panels and fuel cells (FCs), are internally DC. Hence, it is easier for them to be integrated into DC microgrids compared to AC systems. However, DC microgrids have low inertia, and voltage stability is harder to achieve due to the inexistence of reactive power compensators. Therefore, this paper suggests a novel distributed control system for DC microgrids to achieve voltage stabilization and accurate energy balancing of ESSs based on event-triggered average consensus protocol and fractional-order proportional-integral (FOPI) local controllers. The fractional-order controller has many advantages over the integer-order classical controller, such as achieving better performance, flexibility, and a higher degree of freedom in the controller design. In this regard, the FOPI local controllers provide accurate load sharing for the droop mechanism. The distributed secondary layer controller compensates for the voltage offset, meanwhile balancing the energy level of ESSs. In the conventional distributed control strategies, the communication between the agents is periodically at discrete sampling time, resulting in network traffic increase and excessive energy consumption in the devices. Therefore, in this paper, the average consensus protocol is event-based, saving considerable network capacity for other microgrid communication needs. The effectiveness of the proposed control strategy is confirmed by the simulation of a 380V DC microgrid in MATLAB/Simulink that shows the achievement of the control objectives. Simulation results show the proper voltage stabilization and accurate energy balancing of ESSs in both islanded and grid-connected modes and a significant reduction in the transmitted information between agents. [ABSTRACT FROM AUTHOR]

• Proposing Stochastic multi-stage model for distribution system planning. • Applying scenario reduction from historical data by using k-means. • Using convex relaxation to ease simultaneous allocation of RES and storage devices. This paper proposes a mixed integer conic programming (MICP) model to find the optimal type, size, and place of distributed generators (DG) over a multistage planning horizon in radial distribution systems. The proposed planning framework focuses on the optimal siting and sizing of wind turbines, photovoltaic panels, gas turbines, and energy storage devices (ESD). Inherently, renewable energy sources and electricity demands are subject to uncertainty. To handle such probabilistic situations in decision-making, the MICP model is extended into a two-stage stochastic programming model. To obtain more practical results, annual historical data are used to generate the scenarios. For the sake of tractability, the k-means clustering technique is used to reduce the number of scenarios while keeping the correlation between the uncertain data. Due to convexity, the proposed MICP model guarantees to find the global optimal solution. To show the potential and performance of the proposed model a 69-bus radial distribution system under different conditions is dully studied and a sensitivity analysis is conducted. Results and comparisons approve its effectiveness and usefulness. [ABSTRACT FROM AUTHOR]

Highlights • The system is easily generalized to any number of levels. • All switching devices present the same blocking voltage. • The energy storage of the flying capacitors is lower than in a Δ/ Y system. • A modulation technique with optimized harmonics and losses can be used. Abstract Over the years, industrial processes, renewable energy systems and electrification of transport systems have been pushing power electronics equipment towards higher power ratings, quality and faster dynamics. In this context, several multilevel converter structures were developed and became popular, such as neutral-point-clamped, flying capacitor and cascaded half-bridge. This paper presents a generalized n -level converter using the multicell flying capacitor structure in an open-end winding configuration. A mathematical analysis shows that the proposed system presents the same output voltages as a traditional Δ/ Y structure, but with less energy storage in the flying capacitors. The correlation with the traditional structure allows the use of an optimized modulation technique developed for the flying capacitor converter. It results in a voltage output with optimized harmonics, keeps the losses evenly distributed among the switching devices, and balances the voltages in the flying capacitors. Experimental results from a three-level conversion system and simulation results from a five-level structure demonstrate the features of the proposed topology. [ABSTRACT FROM AUTHOR]

Abstract: The penetration of renewable sources (particularly wind power) in to the power system network has been increasing in the recent years. As a result of this, there have been serious concerns over reliable and satisfactory operation of the power systems. One of the solutions being proposed to improve the reliability and performance of these systems is to integrate energy storage devices into the power system network. Further, in the present deregulated markets these storage devices could also be used to increase the profit margins of wind farm owners and even provide arbitrage. This paper discusses the present status of battery energy storage technology and methods of assessing their economic viability and impact on power system operation. Further, a discussion on the role of battery storage systems of electric hybrid vehicles in power system storage technologies had been made. Finally, the paper suggests a likely future outlook for the battery technologies and the electric hybrid vehicles in the context of power system applications. [Copyright &y& Elsevier]

• ESS implementations and PV power prediction are used to improve voltage/power profile of the system. • Quantile nearest neighbour forecasting is a new efficient method utilized for PV output power prediction. • The proposed evolutionary algorithm is also used for optimising the size and location of ESSs in the system. • Simulation results show the efficiency of the prediction model and performance improvement of the microgrid. Energy storage system (ESS) has great importance in saving energy in new power systems. Optimum selection of these elements poses a new challenge to improve the energy management and prevent cost increases in the system. Also, renewable energy resources have been increasingly used in microgrids. The uncertainty and variation of renewable distributed generation (DG) affect the performance of power systems. In this paper, ESS implementations and photovoltaic (PV) power prediction are used to improve voltage/power profile of the system and reduce the total cost of the microgrid. The purpose of this paper is the optimal installation of ESSs in a microgrid to minimize the total cost where quantile nearest neighbour forecasting is utilized for PV output power prediction as an efficient approach. Gathering data of the last samples in time duration can be used for an effective prediction of PV output in this method, which can overcome PV uncertainty due to changes in solar irradiation and other parameters. Artificial neural networks combined with multi-layer perceptron and genetic algorithm are used for optimizing the size and location of ESSs in the system. Simulation results show that the proposed method improves the power profile as 14%, 21% and 28%, relatively to the scenarios of optimal ESS installation without PV prediction, using PV prediction but with no optimal ESS implementation and not using PV- no ESS implementation, respectively. Moreover, the accuracy of the proposed prediction method is more than the gradient-descent and RNN methods by about 12% and 5%, respectively, as shown in the simulation results. Also, the cost reduction of proposed method is enhanced as 24% and 31% relatively to the cases of optimal ESS installation without PV prediction and PV prediction without optimal ESS implementation, respectively. [ABSTRACT FROM AUTHOR]

• It outlines the problem formulation and solution approaches for stochastic UC, cost-based unit commitment (CBUC), and profit-based unit commitment (PBUC) problem. • With the aid of a tree diagram representation, the UC models for the power system in three stages including thermal UC in first stage, security constrained unit commitment (SCUC) and stochastic/robust UC in second stage, and SCUC with contingency and SCUC with uncertainties in third stage are presented. • Additional diagram is shown to represent the various modules that can be integrated with UC models to enhance system performance such as hydro units, FACTS devices, HVDC lines, ESSs, etc. • The solution techniques including relaxation based approaches, mathematical optimization solvers, meta-heuristic approaches, learning based approaches, and other established methods are recapitulated in tabular form. The realistic power systems include several types of controllers in transmission lines, different types of energy storage systems (ESSs), and integration of various renewable energy sources (RESs) with thermal generation units. The unit commitment (UC) problem of practical systems has been the subject of a great deal of research for the past few years. An extensive investigation of the system modeling, solution methodologies and future scope in the area of UC problem is presented in this paper. It outlines the problem formulation and solution approaches for stochastic UC, cost-based unit commitment (CBUC), and profit-based unit commitment (PBUC) problem. With the aid of a tree diagram representation, the UC models for the power system in three stages including thermal UC in first stage, security constrained unit commitment (SCUC) and stochastic/robust UC in second stage, and SCUC with contingency and SCUC with uncertainties in third stage are presented. Additional diagram is shown to represent the various modules that can be integrated with UC models to enhance system performance such as hydro units, flexible alternate current transmission system (FACTS) devices, high voltage direct current (HVDC) lines, ESSs, etc. The solution techniques, including relaxation based approaches, mathematical optimization solvers, meta-heuristic approaches, learning based approaches, and other established methods, are recapitulated in tabular form. [ABSTRACT FROM AUTHOR]

The growing penetration of converter-interfaced renewable energy sources (CI-RES) has revealed several challenges related to the stability of the electrical grids. To mitigate these issues, there is an ongoing research regarding the concept of virtual synchronous generators, where new control schemes are employed to enable CI-RES with integrated fast-acting energy storage system (ESS) to provide ancillary services to the grid, e.g., inertial response (IR). Although several solutions have been proposed in the literature, the proper integration and energy management of the ESS towards IR provision remains an open research topic. In this paper, an holistic control approach is presented regarding IR provision by a system comprising of a CI-RES and an ultracapacitor (UC). The proposed approach is supplemented by an efficient energy management system allowing the UC to release/absorb energy during IR provision while always ensuring the operation of the UC voltage within its technical limits. The robustness and the performance of the proposed method are experimentally evaluated in a lab setup, where all the components of the proposed system, i.e., CI-RES and UC have been analytically implemented. • A comprehensive control scheme for inertial provision from CI-RES is described. • An Ultracapacitor (UC) is used to control the voltage at the dc bus. • An energy management system is applied to control the voltage of the UC. • Experiments are conducted to evaluate the performance of the presented method. [ABSTRACT FROM AUTHOR]

This paper presents a unified unit commitment and economic dispatch model that integrates storage devices for the short-term operations scheduling of power systems with high renewable penetration. The presented model is a single multi-hour look-ahead real-time tool that uses multiple time resolution to contain computational requirements. The decisions for the first time interval are binding while the decisions of the remaining scheduling horizon are advisory. Adopting this approach, storage facilities are more efficiently utilized, by constantly adapting their energy injection/withdrawal schedule based on updated system information, thus, alleviating the problem of defining the appropriate stored energy level during economic dispatch. The proposed model is presented in both deterministic and stochastic frameworks. The operational impacts of storage and the benefits of implementing stochastic optimization are validated via extensive simulations using data from the Greek Interconnected Power System. [ABSTRACT FROM AUTHOR]

• • Compressed air storage alone can firm up wind and solar variability in California. • • Increased penetration of renewable energy can decrease CAES storage requirements. • • Type of renewable energy strongly affects the required CAES capacity. • • Bulk CAES deployment has limited applications due to real-world constraints. Bulk energy storage can play an important role in the decarbonization of renewable-dominant electricity system. It can offer a solution to the grid balancing problem caused by the variability in the output of renewable power generation. This paper explores the requirement for compressed air energy storage (CAES) capacity as the penetration of renewable energy increases to compensate for the variability of wind and solar. A case study for California using parsimonious macro energy model with real-world historical demand and hourly weather data has been utilized to do this analysis. In the least-cost model, with no excess wind and solar power generation, required CAES capacity is 3.71TWh in 100% decarbonized scenario. If a strict rule of net-zero curtailment was in place, the storage capacity required would be 3.2% higher (3.83TWh) with 9.8% increased cost of electricity. However, the required CAES capacity decreases with the excess solar and wind power generation. In case of 2 times increase in the wind and solar potential the required CAES capacity with strict zero curtailment would be 19.2% less (3.10TWh) and the corresponding cost would decrease up to 29.7%. The study also revealed that the type of renewable energy mix (wind, solar or both) has strong effect on the required energy storage capacity for deep decarbonization. The presented results demonstrate that excess wind and solar power generation can be used to significantly reduce the required storage needs for a fully renewable power system at reduced cost. In California, building of a large battery energy storage (up to 3 TWh) is limited by societal, geographical, and economic constraints. This study suggests that in addition to the battery energy storage California may need to look for other dispatchable power sources (or the equivalent in load flexibility) for 100% wind and solar based fully decarbonized power system. [Display omitted] [ABSTRACT FROM AUTHOR]

The advent of renewable energy sources (RESs) has posed several technical challenges related to the operation of modern power systems. These can be classified into two main categories: (a) problems at the power system level, and (b) local problems at the distribution network (DN). To solve these issues, there is a strong need for the development of innovative control schemes that can be employed by converter-based network assets, e.g., battery energy storage (BES) systems and distributed RES. In this context, a unified control system (UCS) for the provision of ancillary services (ASs) by distributed RES-BES systems within a multi-services perspective is introduced in this paper. The proposed UCS comprises different algorithms to provide ASs related to voltage regulation, voltage unbalance mitigation and frequency support. The UCS is implemented in 3-phase, 4-leg converters (3Ph4LCs) to ensure the provision of ASs in unbalanced DNs. In addition, at the dc side of the 3Ph4LC a series dc–dc converter for BES support allows the implementation of the proposed UCS in weak DNs. The transient operational performance of the proposed UCS is evaluated by conducting time-domain simulations. • A unified control system for the provision of ancillary services is introduced. • It employs voltage regulation, voltage unbalance mitigation and frequency support. • The proposed algorithms are integrated into a 3-phase, 4-leg converter. • A power oscillation damping technique is introduced to improve PLL performance. • Time-domain simulation results verify the performance of the proposed control system. [ABSTRACT FROM AUTHOR]

• A compilation of diverse range of resources i.e., PV, wind, fuel cell, CHP is selected to make the VPP system more flexible and robust to perform smooth functioning. • Single objective optimized scheduling is performed separately from both economic as well as environmental point of view in which enhancement in total profit is achieved followed by a reduction in generated emission and a separate framework for multi-objective scheduling is established for the selected problem formulation. • Storage options are provided to ensure the continuity of uninterrupted power supply 24 × 7 and risk management concept is introduced in the selected problem formulation. • Numerical analysis for day ahead scheduling is listed and compared with the already published results. 5 min interval scheduling is also carried out for the first time which is corelated with the proposed work. • Modified nature inspired metaheuristic technique is employed to solve the intended objective function and the quality solution sets are obtained after repeated trials which are compared with recently published work. This paper addresses optimal scheduling of a virtual power plant (VPP) to enhance the economic and environmental aspect of the anticipated VPP network comprise of renewable sources i.e., solar PV, wind power and fuel cell (FC) along with a cogeneration combined heat and power (CHP) unit. A provision of storage is also provided in the form of electric vehicle (EV) as a flexible reserve and energy storage system (ESS) as a spinning reserve to increase the system reliability and security. To improve the economy, risk management concept is incorporated in which a popular risk measure technique conditional value at risk (CVaR) is also utilized to ameliorate low profit scenarios by employing a modified version of recently developed Harris Hawk's optimization (MHHO). A comprehensive analysis is conducted by considering two types of scheduling into account i.e., day ahead scheduling followed by 5-minute interval which has been developed for the selected problem formulation. Single as well as multi-objective optimized scheduling is performed and numerical results are compared with the published work. The developed approach responds well to the proposed formulation and the statistical results indicates the effectiveness and suitability of the anticipated technique in terms of net-profit and environmental feasibility. [ABSTRACT FROM AUTHOR]

• Wind power generation model based on deep neural learning. • Generation optimization of combined operation of wind power-pumped storage-hydrogen energy storage. • The simultaneous optimization of control and design of the combined system. Multi energy complementary system is a new method of solving the problem of renewable energy consumption. This paper proposes a wind -pumped storage-hydrogen storage combined operation system based on deep learning and intelligent optimization, which introduces deep neural network to predict wind power generation. With the goal of minimizing power fluctuation and maximizing economic benefits, the system is optimized by multi-objective genetic algorithm for the basic parameters of wind turbine arrangement, electrolyzer and pumped storage power station. After getting the Pareto frontier solutions, we use Technique for Order Preference by Similarity to Ideal Solution(TOPSIS) to select the best scheme. Taking a specific case study for example, the system reduces the daily power fluctuation from 104.20 MW to 23.37 MW, a drop of 77.60%, and produces daily economic benefit of 139,638.5 yuan. Finally, by comparing the system with 3 and 9 wind turbines, we confirm the flexibility and universality of our system. [ABSTRACT FROM AUTHOR]

• This review paper sheds light on the important optimization techniques used in power system operation. Optimization techniques are a critical key in the operation strategies for a hybrid energy system. This paper covers the recent optimization approaches in the renewable energy domains, providing a systematic techno-economic operation of combined PV, diesel turbine generator, and energy storage systems. This review paper has discussed the general optimization formulation framework which covers the optimization decision variables, constraints, and objective functions for a better understanding of the operation problem. Then, it reviews the deterministic, stochastic, and hybrid approaches to energy system optimizations and contrasts their advantages and disadvantages. The deterministic method is not efficient for the large power systems, so stochastic methods are becoming more popular. As the complexity of power system operation grows, the hybrid technique is also introduced to solve non-convexity problems. Ultimately, this review article offers a good understanding in the scope of operation optimization of hybrid energy systems. Humanity is presently confronting a global energy crisis this is due to an insufficiency in traditional energy sources while the energy demands are worldwide increased. In response to this crisis, improving energy efficiency and sustainability and are becoming significantly important. Therefore, distributed energy systems are extremely important in terms of having multiple energy sources to meet time-varying consumer demands. Recently, hybrid distributed energy system has become widely applied to raise system reliability and improve power quality instead of depending on one type of conventional energy sources such as diesel generators. Yet, it faces some challenges in its widespread deployment such as unpredictable nature and intermittently varying renewable energy sources like solar radiation. This issue can be overcome by integrating energy storage that manages the time mismatch between energy production and load requirements. Hence, it is important to review the recent promising approaches for the optimization of hybrid systems to reduce the operation cost and total network losses. This work encompasses a review of the recent optimization approaches for hybrid energy systems with PV, diesel turbine generators, and energy storage systems to give the basis for solving difficult and intricate real-world problems related to distributed energy system operation. [ABSTRACT FROM AUTHOR]

This paper addresses passive and active preventive maintenance scheduling in an islanded microgrid with storage and renewable energy sources. At first, under a centralized framework, a single-level cost-minimization formulation for passive maintenance scheduling is developed and used as a benchmark in the operation. An independent microgrid operator is responsible for the operation in this framework. Then, through a bi-level formulation, the active maintenance scheduling and operation is carried out with profit-maximization objective. These two developed frameworks provide the houses with opportunity to earn profit and the regulator and the operator to analyze the performance of the system. The bi-level formulation is transformed into a single-level problem through Karush–Kuhn–Tucker conditions. Furthermore, the proposed model provides the capability of incorporating condition monitoring data into the operation. The model is validated through a test system and the outcomes demonstrate the advantages, applicability and challenges of utilizing the proposed model. [ABSTRACT FROM AUTHOR]

Renewable energy sources (RES) are becoming an important part of energy continuity for today’s electrical power grid, since RES are intermittent and unstable. Energy storage technologies are the only solution for this energy sustainability problem. In this study, a new Smart Energy Management Algorithm (SEMA) is proposed for Hybrid Energy Storage System (HESS) supplied from 3-phase 4-wire grid connected photovoltaic (PV) power system. HESS consisting of battery and ultra-capacitor energy storage units is used for energy sustainability from solar PV power generation system. Several different operation cases in HESS have been analyzed and experimentally tested by using the proposed SEMA. In experimental tests, load status of one sunny day and PV power profile have been created and tested dynamically by using SEMA and some of the test results in eight different operation modes are given in this paper. The battery group is charged with 1320 W power by the system and remaining energy is transferred to the grid with 5% current harmonic via the inverter in one of the operation modes. The HESS is the most effective energy storage system due to its high power density, fast response, and high efficiency. The proposed system has been verified simulations results and experimental tests. [ABSTRACT FROM AUTHOR]

Integrating electric vehicles (EVs) into a microgrid can provide additional energy and storage to the microgrid. The benefits depend on factors such as market prices, EVs’ state of charge, and their arrival/departure times. In this paper, it is assumed that the microgrid operates in a grid-connected mode and consists of thermal units, renewable energy resources and a parking facility. An optimization model for the energy management that considers the energy and storage provided by the EVs is proposed. The objective function of the model is to minimize the expected total operation costs including generation, day-ahead market, battery wear, and real-time balancing costs for the next 24 h. The uncertainty of the demand and the available EVs in addition to the intermittency of the renewable energy resources are taken into account. The model is solved using the Benders decomposition algorithm and results are obtained using a 14-bus distribution test system. The results show that using the storage and energy of the EVs reduces the total operation cost of the microgrid. [ABSTRACT FROM AUTHOR]

• A hybrid hierarchical bottom-up electricity trading scheme including distributed P2P trading is introduced in a fully transactive smart grid environment. • Every end-user is simulated as a separate entity. • Enhanced clearance of generation is achieved through hierarchical and parallel auctioning procedures at all possible power system levels. • The privacy of end-users is ensured as no cooperative games are employed and all end-user offers are calculated locally. • A hybrid day-ahead market using the proposed energy trading scheme can lead to significant energy cost reduction and load variation moderation. Smart Grid concept has promoted the use of distributed generation in an era of continuously increasing renewable energy generation. Peer-to-Peer electricity trading is a means for smart grid to achieve it. Consumption-side is able to directly trade electricity and enhance power system flexibility. However, the stochastic behavior of prosumers and renewable generation introduces great complexity that needs to be addressed through careful modeling. This paper proposes a hierarchical electricity trading scheme that models residential, commercial and industrial prosumers, energy storage, renewable energy sources, and centralized generation in a fully transactive smart environment. To model the above, a hierarchical MAS of three auctioning layers is developed. A novel modeling of residential activity and optimal constrained end-user energy scheduling are developed. Moreover, a parallel peer-to-peer electricity market that uses the Discriminatory k-DA matching mechanism and enhanced price calculation through fuzzy logic is developed. A novel case study of a hybrid Day-Ahead market is presented, using the proposed trading scheme that yields less expensive electrical power system operation. The proposed detailed model incorporates multiple end-user types and multi-layer scalable energy systems and sets the foundation for holistic energy market modeling. [ABSTRACT FROM AUTHOR]

With the continuous reform of the world's energy system, the energy microgrid built to achieve green, flexible, autonomous and sustainable development of highway is facing new challenges in energy dispatching and management due to the uncertainty from both the supply and demand sides. In this paper, an enhanced coordinated energy scheduling scheme is proposed for typical highway demand scenarios, based on the introduction of mobile energy storage system, to replace the traditional centered power scheduling. It could maintain the balance between energy supply and users demand, and minimize the cost of energy system dispatch operations. The appropriate selection and cost of the mobile energy storage system are investigated and evaluated. Utilizing the data from the designed 30% renewable energy highway service station construction project in Xinjiang, China, the effectiveness of the proposed mobile dispatching scheme is verified. The proposed scheme provides a feasible solution and forward-looking guidance for the integration of highway transportation energy nexus. [ABSTRACT FROM AUTHOR]

• Joint planning of capacitor, dispatchable DG, and energy storage with renewables. • Improve technical and economic effects of Volt-VAR optimization for energy saving. • High accuracy and low computational burden through MultiGMM-based data cluster. • Adaptive to scenario-based planning problems in large-scale distribution systems. • Provide incentives for distribution companies to invest in Volt-VAR optimization. With recent installations of intermittent renewable energy generations in electric distribution systems, their impacts on classical Volt-VAR optimization (VVO) schemes, especially the energy-saving effects, are becoming one of the major issues. The optimality of VVO schemes is hard to be guaranteed due to voltage fluctuations caused by distributed renewable generations. Distributed energy resources (DERs) could be a solution to provide flexibilities to improve VVO performance if the high cost is lowered while their locations and sizes are optimized. In this paper, an optimization model is proposed for a joint planning of capacitors, dispatchable distributed generators, and energy storage to improve VVO energy-saving effects while minimizing the system operating cost. The uncertainties of renewable energy resources, electricity demand, and electricity prices are accurately modeled benefited from emerging data analysis technologies. The technical and economic effects of the proposed model are proved and tested using real data sets and compared with classical models. The high precision of the proposed model is verified using time-series simulations. [ABSTRACT FROM AUTHOR]

• Modeling the optimal protection coordination strategy for grid-connected and islanded micro-grid based on the daily data of optimal operation of this network. • Considering RES and ESS simultaneously in the OPC method, where previous researches select only the constant capacity for DGs. • Investigating the impact of the energy management for DGs and ESS on the OPC using the protection coordination index. • Using reliable and secure solver such as CSA with low standard deviation in the large scale problem of the OPC method. This paper presents the optimal protection coordination (OPC) to grid-connected and islanded micro-grid (MG) consisting renewable energy sources (RESs), and energy storage system (ESS). The proposed approach considers objective function as minimizing the total operation time of the dual setting overcurrent relays (DSORs) in primary and backup protection mode. Problem constraints are the regulation parameters limits of the DSROs, sizing limit of FCLs and also the coordination time interval (CTI) inequality. MG includes generally RESs and ESS, hence, this problem needs to the daily optimal operation results of MG in both grid-connected and islanded modes to obtain the network variables before the fault occurrence. Therefore, it is minimized the MG operation and load shedding costs of islanded MG while it is constrained to MG optimal power flow equation in the presence of RESs and ESS. The proposed OPC includes non-linear model, hence, this paper uses the crow search algorithm (CSA) to obtain a reliable and optimal solution for this formulation. Finally, this strategy is implemented on the 9-bus and 32-bus MGs, thus, it can obtain the high quick protective solution for these MGs by selecting optimal regulation parameters and sizing to DSORs and FCLs, respectively, according to numerical results. Image, graphical abstract [ABSTRACT FROM AUTHOR]

An increasing number of single-phase loads and renewable energy resources (RESs), such as single-phase rooftop PV units, are unevenly distributed in low voltage (LV) distribution networks. This exacerbates unbalanced conditions in the network which in turn adversely affects the power quality, stability and reliability. This paper presents a comprehensive review of the state-of-the-art techniques for alleviation of the unbalanced conditions in both three-wire and four-wire LV three-phase distribution networks. Technical issues arising from the unbalanced conditions are highlighted. Several definitions of the unbalance indicators and standards that are widely used for unbalance analysis are also given. Furthermore, recent unbalance compensation techniques based on three devices; namely, power electronic converters, electric vehicles (EVs) and energy storage systems (ESSs) are critically reviewed. It is found that the power electronic converters with suitable control strategies and management of EV chargers and plug-in electric vehicles (PEVs) have been intensively studied so far. However, based on the literature survey, there is currently no work on applying cutting-edge methods, such as the distributed cooperative control strategy and multi-agent deep reinforcement learning (MADRL), to utilize distributed single-phase ESSs to mitigate the unbalanced conditions. Therefore, key challenges, possible unbalance mitigation techniques and future research directions are recommended and discussed. [Display omitted] • Techniques for alleviating unbalanced conditions in LV systems are reviewed. • Three devices with recent techniques for compensation are presented. • Indicators and standards for analysis of unbalanced conditions are provided. • 1-phase ESSs is recommended with new strategies for future research directions. [ABSTRACT FROM AUTHOR]

• ESS scheduling has been performed in both weekly and daily modes. • Different operating strategies has been assessed. • Stochastic models of parameters in the MO scheduling are taken into account. • Impact of parameters of EES on reliability determined. Energy storage systems (ESSs) are useful devices to ensure the reliable operation of microgrids especially those with high penetration of renewable energies. The microgrid operation is highly associated with scheduling of ESS units. Therefore, in this paper, a new algorithm for ESS scheduling has been suggested in order to manage MG in a reliable manner. Because reliability considering and cost minimization are conflicting objectives in ESS scheduling, the multi-objective optimization problem should be solved for optimal scheduling of ESS. Different operating strategy have been considered and their impact on ESS scheduling in the microgrid has been investigated. In order to properly consider the uncertainties associated with the multi-objective scheduling problem, probabilistic models have been presented for the parameters in the network and they are expressed as mixed integer linear programming (MILP) problems. Non-dominated sorting teaching learning-based optimization (NSTLBO) algorithm is employed to solve the MO problem. Scheduling plan is performed on both weekly and daily horizons in connected/islanding microgrid modes. By implementing this method on a modified 33-bus IEEE test system, the results endorse the effectiveness of the proposed scheme for enhancing the reliability of MGs. [ABSTRACT FROM AUTHOR]

• A new clustering-based stochastic method for optimized operating of SRSs. • Considering the RBE, RERs, load, and number of passengers' uncertainties. • Applying the proposed method to an actual SRS in TUSROC. • A related error under the RPC can be less than 4.4% by the proposed fast method. • Studying various operating modes of RBE (direct and indirect transactions by the ESS). The smart railway stations (SRSs), as prosumer microgrids, are considered active users in smart grids. By utilizing regenerative braking energy (RBE) and renewable energy resources (RERs) along with energy storage systems (ESSs), these SRSs can participate in the prosumer market. The uncertainties of RERs in SRSs due to meteorological factors have been studied in the literature. However, there is a research gap in developing a stochastic method for optimized operating of SRSs considering the RBE uncertainties besides the RER, load, and number of passengers' uncertainties. In this paper, a new probabilistic clustering-based framework for the optimal operation of SRSs is presented. By applying Monte Carlo Simulations (MCS), several scenarios are generated and then clustered by the k-means algorithm. The introduced method is applied to an actual SRS in Tehran Urban and Suburban Railway Operation Company. The test results of the MCS, deterministic, and proposed scenario-based approaches are compared to illustrate the proposed method. Test results imply that the related error of the scenario-based method under the real-time pricing can be less than 4.4%, while the computation time significantly decreases. Furthermore, sensitivity analysis is done to determine how the exchanging power constraints and ESS capacity might influence the SRS operation. [ABSTRACT FROM AUTHOR]

• A stochastic economical flexibility evaluation method is proposed. • The probability distribution function of the network's flexibility is provided. • The flexibility that storage systems provide for distribution networks is quantified. • The PV generation and energy storage impacts on the flexibility are shown. • The storage capacity should be adopted for the secure distribution grid operation. Deriving benefits from a variety of flexible sources to lower or even eliminate renewable energy resources challenges is an imperative issue in the management of power systems. Constructing a well compatible flexibility evaluation method that encompasses cost minimization and flexibility improvement is crucial to tackle this. In this paper, a stochastic economical flexibility evaluation method is proposed based upon the envelope of the feasible operation region to the uncertain space. The probability distribution function of the network's flexibility is provided for each hour. Furthermore, the flexibility that storage systems as a flexible source provide for the distribution networks is quantified. The flexibility sensitivity to the storage facility capacity is scrutinized. The simulation results show the impact of PV generation and energy storage systems on the flexibility improvement. Moreover, the sensitivity outcomes reveal that which storage capacity should be adopted for the secure distribution grid operation. [ABSTRACT FROM AUTHOR]

This paper proposes a unified framework between the optimal energy management (OEM) problem of grid-connected microgrids and the frequency control of power systems through time-domain simulations. The proposed formulation is aimed at analyzing how different objectives for the microgrid OEM and control strategies affect the system frequency regulation. Each OEM model minimizes the microgrid local objective while ensuring energy reserves and operational limits by solving a two-stage stochastic optimization mathematical model. Two control approaches of microgrids are considered, namely, greedy and cooperative. The proposed framework is evaluated considering the IEEE 39-bus test system, including multiple microgrids with different energy storage capacities, and control strategies. Results show an improvement in system's frequency deviation when MGs operate in the cooperative configuration while simultaneously reducing their operational costs. • An aggregated microgrid (MG) dynamic model is introduced. • Decentralized stochastic optimization for optimal energy management (OEM) of MGs. • Unified OEM of MGs and power systems frequency control via time-domain simulation. • Two control OEM strategies for dispatch planning (greedy and cooperative). [ABSTRACT FROM AUTHOR]

Renewable Energy Sources (RES) plants still have to operate at their maximum possible output and are therefore independent of electric energy consumption. Thus, coordination between distributed generators, energy storage systems and flexible loads is expected to add a great value to grid operations and facilitate further RES penetration. The increasing penetration of bus communication technologies into buildings and their control possibilities over electric energy consumption may also make valuable contributions to the grid operation. Thus, the control system of the future grid should be compatible to smart buildings and cities. This paper presents a control system of an experimental microgrid which is made compatible to bus communication technologies at the demand-side, by applying a simple technique. The proposed technique does not require additional interface or software tools, as other methods do and therefore is a cost-effective method and can be easily expanded and used for demand-side management in future smart grids. Experimental results prove that bus communication technologies could contribute to the voltage regulation as well as to the application of efficient energy management policies of microgrid-based smart grid topologies. [ABSTRACT FROM AUTHOR]

• An efficient energy management method using DMRFO is proposed. • The hybrid LAES-HTES storage is implemented to minimize cost and environmental issue. • The point estimate method and DMRFO algorithm is used to model the uncertainty factor. • Renewable energy sources and electric vehicle are used in micro grid structure. Different renewable energy sources, active loads, electric vehicles, and storage devices make an independent microgrid (MG). Energy management of MG is an important issue. In this paper, efficient energy management (EM) of a microgrid using a novel method based on a new meta heuristic algorithm is proposed. Proposed MGs include renewable energy resources (wind turbine, fuel cell, and solar cell), plug-in hybrid electric vehicles (PHEVs), and liquid air energy storage (LAES) combined with high-temperature thermal energy storage (HTES). In order to carefully investigate and improve the management method, uncertainty parameters such as wind speed, solar radiation, load demand, and energy price is modeled using a probabilistic approach based on the point estimate method (2m + 1) and the developed manta ray foraging optimization (DMRFO) algorithm for posture forecasting with an uncertainty-weighted measurement error of feature objective is used to solve the EM algorithm. The multi-objective optimization problem presents an improved energy management method considering cost minimization and pollution as objective functions. Afterward, the function of the proposed algorithm is compared with that of other methods and, then, the superiority of the proposed method is confirmed. Finally, it is proved that by using the proposed model, total cost and emission is reduced about 3.5% and 21.33%, respectively. [ABSTRACT FROM AUTHOR]

• The UC with ESS and re-entry of power line variables with FACTS connections • Analyzing the effects of ESS and FACTS systems separately • Sensitivity analysis of wind power penetration level on economy and operation of REs This study investigated the unit commitment (UC) programming in the presence of both wind energy and energy storage sources. Programming this problem provides an optimal timetable for generation unit commitment to maximize security and minimize costs, as well as meet the grid and unit constraints in a period, as a major research topic. Today, the relationship between energy storage units and the grid has received considerable attention of researchers and grid operators. As the flexible alternating current transmission system (FACTS) devices promote the security of transfer lines, the UC problem was solved in the presence of FACTS devices in this paper. The salient feature of the proposed model is the no requirement of predicting uncertain variables and parameters; instead, uncertain parameters were defined in a "certain interval". Then, these intervals were sequentially divided into smaller intervals, so that a set of robust integer programming problems can be defined and solved more easily. Another feature of the robust optimization method is to obtain the possible and optimal solution for the worst-case scenario for uncertain parameters in the defined uncertainty interval. By solving the proposed model, sufficient information was created directly for forming the units' hourly bidding curves. The proposed model was discussed in different scenarios and the results indicated its proper performance. [ABSTRACT FROM AUTHOR]

This paper evaluates the economic sustainability of different premium power park (PPP) solutions – derived from two prototypes and a theoretical solution – and completes a previous technical analysis of the same PPP solutions. All of these are utility-provided solutions that improve the quality of the electricity delivered at medium voltage (MV) level. We calibrate the study on the potential customers of a PPP, which are MV users sensitive to power quality (PQ) events and badly served. As to PQ events, accidental interruptions and voltage dips are concerned. We evaluate the direct costs caused by PQ events through original and simple cost models developed using different approaches. Even though until today PPPs have not proved to be a winning idea, the results prospect possible future applications of this idea as a consequence of the increasing penetration of dispersed generation, maturation of renewables and developments of energy storage technologies. [ABSTRACT FROM AUTHOR]

This paper describes an economic dispatch formulation for a power system control area with high penetration of renewable generation and energy storage. We develop a comprehensive objective function that is unbiased to both the conventional and renewable generation sources. Operating constraints of conventional sources are considered, wind turbine generation is treated as a dispatchable source, and solar photovoltaic generation as negative load. A simulated annealing and particle swarm optimization algorithm are separately applied to minimize the objective function. The results obtained by the proposed economic dispatch formulation show that even a variable energy resource like wind can be dispatched in a manner that benefits the control area as a whole. [ABSTRACT FROM AUTHOR]

• Smart virtual energy storage system is developed by using demand response management • Regression based artificial neural network (ANN) model is proposed to predict the discharging capacity of aggregated air-conditioners • Stochastic gradient descent optimization algorithm is implemented in a back-propagation network to achieve minimum loss in predicted result • Virtual energy storage capacity is estimated to reduce battery sizing [Display omitted] Integration of renewable energy sources (RES) poses numerous challenges in smart grids, as RES are largely dependent on natural intermittency. Hence, it is necessary to rely on smart energy storage systems (SESS) to mitigate the fluctuation of RES on the supply side. SESS can be achieved by using demand response management (DRM), i.e., by aggregating thermostatically controlled loads using state-of-art smart grid technologies. In this paper, the air conditioners (ACs) are aggregated into a virtual energy storage system (VESS) by employing an electric model of the ACs. A simple mathematical model was described to evaluate the charging and discharging pattern of the load in terms of power and energy. Based on the VESS, the temperature control strategy was designed to reduce the power consumption of the ACs when making sure the room temperature is below the permissible limit. An artificial neural network (ANN) model was designed to predict the energy capacity of ACs, using which the VESS is achieved. Besides, the stochastic gradient descent (SGD) optimization algorithm was implemented in the back-propagation of the ANN model to achieve the optimum prediction values. Four case studies were presented to demonstrate the energy-saving capacity of ACs by regulating the set point temperature of a selected site. The results illustrate the change in the power consumption pattern of the ACs with and without employing them for virtual energy storage (VES). Hence, the proposed ANN model enables the aggregators to achieve the desired VES without affecting the comfort of the occupants. [ABSTRACT FROM AUTHOR]

• Two-timescale volt/var optimization that utilizes distributed energy resources. • Coordinating distributed energy resources along with utility-owned assets. • Engaging utility operator in the decision-making process. • Active distribution network with high distributed energy resources penetration. • Incorporate all resources and assets for enhanced network performance. The high penetrations of distributed energy resources of renewable energy sources, battery energy storage systems, and electric vehicles introduced several challenges to power networks. It can lead to high voltage variation, sudden over/under-power generation, high losses issues, and negatively impact distribution assets. Thus, there is a vital need for volt/var optimization schemes that integrate conventional volt/var devices with inverter-interfaced resources to overcome these challenges. This paper demonstrates a performance assessment of the volt/var optimization scheme utilizing different utility-owned assets and resources based on a two-timescale multi-objective algorithm. At the slow timescale, it utilizes capacitor banks and on-load tap changers to minimize system losses and maximize asset lifetime based on their conditions. The algorithm incorporates the utility operator's direct input for setting the conventional volt/var devices. The operation of these devices is optimized, in three steps, by limiting the number of switching operations based on the operator preferences. At the faster scale, it considers renewable energy sources, electric vehicle charging stations, and battery energy storage systems to be optimized to limit voltage variation and system losses further. The IEEE 33-bus distribution network is modified and used to demonstrate the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

• Study the vision of engaging networked data centers into distributed energy markets. • Propose a decision-making model to optimize the bidding decisions and energy resource operations of networked data centers in multiple geo-distributed local energy markets. • Conduct comprehensive numerical simulations to validate the benefit the cloud service provider can obtain from local energy markets. Active distribution network Internet Data Centers (IDCs) are large energy consumers in modern distribution networks. The integration of renewable energy and energy storage systems transforms IDCs into proactive energy prosumers, and enables them to participate in the energy trading in regional local energy markets. This paper proposes an energy management framework for a Cloud Service Provider (CSP) that manages multiple geographically distributed IDCs. The framework provides decision-making support for the CSP to optimally schedule the IDCs' cyber-energy resources (computing requests, on-site battery energy storage systems, and on-site renewable energy sources). In this way, the proposed framework facilitates the CSP to proactively participate in the local energy markets situated in the regions that host the IDCs, with the objective to minimize the CSP's total cost over a finite operation horizon. We use an effective evolutionary computation algorithm to solve the proposed energy management model and design extensive numerical simulations to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: The introduction of renewable energy sources, particularly wind power, is limited by their dependence on weather conditions and by the difficulty of storing surplus energy for use at times when production is low. One effective way of tackling the energy storage problem is to minimise the need for storage, i.e. to switch from a system based on producing electricity in response to the unpredictable whims of demand to one in which consumption adapts to supply. Demand can be managed indirectly via the sending of price/consumption volume signals. This paper presents a mathematical model for forecasting the aggregated electricity demand of a group of domestic consumers signed up to an incentive-based demand management programme. Under this programme consumers receive signals that offer financial incentives for limiting their volume of consumption at time intervals when system peak demand is forecast. The resulting optimisation model is a mixed-integer linear programming problem implemented in JAVA and solved using free software. This model is applied to a case study in which the objective is to limit consumption by a population of 15932 consumers from 15:00 to 17:45 on a specific summer day. The responses to two different incentive amounts are shown. [Copyright &y& Elsevier]

• Proposes a modified start and end-time netload uncertainty based multi-interval FRP design. • FRPs are modeled from energy storage systems, demand response and conventional units. • Flexibility constrained scheduling co-optimizes FRPs with energy and ancillary services. • Netload scenarios generated using Markov Chain Monte Carlo technique. • 15%--20% reduction in FRP procurement with proposed FRP design. Flexible Ramp Products (FRPs) can address netload variability and uncertainty caused by increasing renewable energy integration. They secure additional ramping capability to follow netload variations of upcoming dispatch intervals. The challenges in current practice of FRP procurement are - 1) Neglecting start-time uncertainty of netload results in inaccurate ramp requirement estimation 2) Inadequate ramping capacity in real-time market for FRP provision leads to the commitment of out-of-merit units 3) Priority in dispatch of renewable energy sources de-commits Conventional Generation (CG) units, thus reducing available ramping capacity. Hence, scheduling FRPs in day-ahead market from CG units and flexible resources along with an appropriate estimation of FRP requirement is essential to ensure ramping sufficiency. In this context, the paper proposes a modified start-time and end-time netload uncertainty based FRP design and a two-stage scheduling framework to integrate FRPs in day-ahead market. Further, system-wide benefits of integrating flexible resources, FRPs and improved operations are highlighted. The proposed framework is simulated on IEEE RTS-24 and reduced Great Britain test systems and proficiency of the method is compared with existing FRP designs. Numerical results indicate a reduction in operating costs and ramping insufficiency with coordinated operation of flexible resources and FRP deployment. [ABSTRACT FROM AUTHOR]

• Modeling of severe wholesale electricity price (WEM) uncertainty. • Analysis of renewable energy and battery storage on LSE's decisions. • Analysis of change in correlation between demand and WEM price on LSE's decisions. In any retail market, Load Serving Entities (LSE) and consumers can benefit from an effective dynamic pricing mechanism. Consumer's price responsiveness and uncertainty of Wholesale Electricity Market (WEM) prices can offer unexpected profits for LSE. LSEs are incentivised to procure a portion of their energy demand from Energy Storage Systems (ESS) due to increasing renewable penetration at the distribution end. LSE's decisions become complex while handling uncertainty arising from such constantly varying conditions. Sale price dynamics optimization could minimize undesirable impact of these uncertainties and maximize LSE's profit. However, this optimization is subject to severe WEM price uncertainty. In this perspective, this paper proposes a sale price dynamics optimization model to maximize LSE's profit while immunizing LSE against severe uncertainty of WEM prices. Information Gap Decision Theory (IGDT) framework is considered to explicitly model severe price uncertainty and determine robust and opportunistic decisions. Impact of Renewable Energy (RE) availability along with Battery Energy Storage (BES) operations on procurement decisions is analyzed. Results highlight that the proposed model ensures consumer benefit and increases LSE's profit. Correlation variation between demand and WEM price profile establishes effectiveness of the proposed model. RE and BES enhance robustness of procurement decisions. [ABSTRACT FROM AUTHOR]

• Modeling the multi-layer EMS in the GCMGs based on coordination between individual MGs and MGC, and MGC and DSO to reduce the data volume in the DSO and obtain high performance speed. • Presenting two-stage operation according to hourly day-ahead and 5 min real time energy markets model to achieve the accurate and optimal economic formulation. • Considering optimal energy management model for GCMGs including high penetration rate of RESs and EVs, and. • Modeling the different uncertain parameters by stochastic programming that is coupled the MCS and fast backward/forward approach. In this paper, two- stage operation of a grid-connected microgrid (GCMG) with high penetration rate of renewable energy sources (RESs) and electric vehicles (EVs) is presented based on day-ahead and real time energy markets, where GCMG follows multi-layer energy management system (EMS). In the proposed method, all microgrids (MGs) are categorized to individual MGs and an MG community (MGC) connected to the distribution network which manages other MGs. Hence, the first/second layer of EMS is applied to individual MGs/MGC according to hourly operation in the day-ahead market at the first stage of the problem. The first layer model minimizes the operating cost of the MG subject to network model, distributed generations (DGs), energy storage systems (ESSs) and EVs parking lot constraints in individual MGs. The second layer model minimizes the sum of expected operation and risk costs of the MGC, limited by the same constraints of the first layer problem. In the second stage, the imbalance cost between day-ahead and real time operation is minimized, constrained to MGs and their devices model based on 5 min real-time dispatch. Stochastic programming based on coupling Mont Carlo Simulation (MCS) and fast backward/forward approach is used to model uncertainties of load, renewable power, energy price, and EVs parameters. Therefore, multi-layer energy management, coordination and RESs and EVs in GCMG, two-stage operation including day-ahead and real-time scheduling, and the procedure used for stochastic modeling of uncertainties are among the contributions of the proposed scheme. Finally, to evaluate the efficacy of proposed approach, it is tested on a standard system in GAMS software. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract: The power output of generators based on renewable energy sources is often difficult to predict due to the non-deterministic behaviour of the energy source. Particularly in the case of wind turbines this leads to unpredicted line loading and requires balancing energy, at relatively high costs, depending on market structures. Consequently, the income from the production from such non-dispatchable generators can be significantly reduced by the penalty costs incurred. This paper investigates the potential of operating an energy storage device in parallel with the non-dispatchable generator in order to compensate the inaccuracies of the forecasted infeed and to avoid infeed deviations. A time series based simulation methodology is discussed, suitable for any type of non-dispatchable generator. The methodology contains a procedure for simulating different forecast errors, applying an exponentially weighted moving average approach. Analysis procedures and system performance indices are introduced for the evaluation of the configuration’s performance. The applicability is shown in two case studies, using measurement data from a wind turbine and from a photovoltaic system. Both case studies show that the suggested configuration considerably improves the reliability or dependability of the network infeed, in turn reducing the demand for balancing energy and back-up generation. The relation between forecast error magnitude and required energy capacity is identified and the coherence of the time series analysis is discussed. [Copyright &y& Elsevier]

• Minimization of the operation cost of a microgrid of buildings, RES, ESS and PEVs. • Operation constraints are fully satisfied and a lower nominal power HVAC is needed. • Autonomous operation is ensured without the use of any emergency generator sets. • A computationally-light equivalent model for dynamic energy storage facilities. • Significant operation cost reduction is achieved for complex microgrids. In this paper, an easy to apply and computationally efficient energy management system (EMS) for microgrids of building prosumers is proposed. It is based on a hierarchical multi-agent system (MAS), aiming to minimize the operation cost of a microgrid comprising a group of buildings, renewable energy sources (RES), energy storage systems (ESS) and plug-in electric vehicles (PEVs). The optimization problem is suitably set up to ensure low computation requirements and it is solved with particle swarm optimization (PSO). Optimal operation scheduling for grid-connected and autonomous operation of microgrids of building prosumers, considering their thermal and electrical systems, integrated RES, conventional and dynamic ESS is proposed. The models used are not computationally demanding, allowing the application to complex systems and they are not being limited to one building scale which is the case for the majority of relative research. Another innovative feature of the proposed algorithm, among others, is that it ensures autonomous operation at certain time periods without the use of any emergency generator sets. Detailed simulation results of different operation scenarios, showed that cost savings in range of 11% can be achieved while a large number of operation and technical constraints are satisfactorily met in grid-connected and autonomous operation. [ABSTRACT FROM AUTHOR]

• The optimal allocation of ESSs is determined to achieve the dispatchability of an ADN. • The control-aware approach is embedded for the maximum exploitation of ESSs capacity. • The benefit of ESSs is evaluated through the ADN operation modeled by the PWL-OPF. • The Benders decomposition is applied to solve the complexity of the planning problem. This paper proposes a procedure for the optimal siting and sizing of energy storage systems (ESSs) within active distribution networks (ADNs) hosting a large amount of stochastic distributed renewable energy resources. The optimization objective is to minimize the ADN's day-ahead computed dispatch error. The allocation of ESS is determined while taking advantages from their operational features regarding the ADN's dispatchability. The proposed ESS planning is defined by formulating, and solving, a scenario-based non-linear non-convex optimal power flow (OPF). The OPF problem is converted to a piecewise linearized OPF (PWL-OPF). The ESS control strategy is designed to fully exploit the energy capacity of the ESS. It is integrated within the PWL-OPF to achieve the ADN's dispatchability regarding all operating scenarios. The Benders decomposition technique is employed to tackle the computational complexity of the proposed planning problem. The problem is decomposed into two sub-ones: a master problem where the allocation of the ESSs is decided, and several subproblems where the dispatchability of ADN with the support of the allocated ESS is evaluated through the scenario-based OPF. To validate the proposed method, extensive simulations are conducted on a real Swiss grid embedding significant PV generation capacity. [ABSTRACT FROM AUTHOR]