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The state-of-the-art centralized computing framework applied to the optimal market dispatch of energy storage systems (ESS) aggregates data from local ESS units for training on the cloud server due to the limited computing resources on edge. However, this approach poses several challenges, including the lack of joint optimization for multiple ESS units, susceptibility to single point of failure and attacks, and inadequate data privacy protection for ESS owners. This study proposes an adaptive personalized federated reinforcement learning (FRL) for multiple-ESS optimal dispatch in various electricity markets with electric vehicle and renewable energy, achieving both the joint optimization of multiple ESSs and avoiding the degraded performance of FRL's local model. Under an adaptively ESS-related differential privacy protection, local devices and the cloud server are specialized to form multiagent deep reinforcement learning (DRL) model for bidding energy, regulation, and third-party services and update the global models, respectively. Given the adaptability of personalization layer to different agents and clients, an adaptive personalization method is developed by calculating the number of personalization layers with the relative loss of each agent and client in the iteration process. The case study shows that the adaptive personalized FRL outperforms conventional FRL, DRL and optimization algorithms. [Display omitted] • FRL-based algorithm for multiple-ESS optimal market dispatch. • Multiagent DRL model for bidding energy, regulation, and third-party services. • Adaptive personalization layers with the relative loss of each agent and client. • Adaptively ESS-related differential privacy method. [ABSTRACT FROM AUTHOR]

Distribution system operators aim to improve hosting capacity (HC) of distribution networks (DNs) to accommodate more distributed rooftop photovoltaics (PVs). Although PV power generation delivers numerous benefits, power unbalance and voltage rise are two major obstacles that limit the network HC. To mitigate these issues, battery energy storage systems (BESSs) can be applied. Thus, this paper proposes a robustly optimal allocation method for BESSs, which aims to reduce the power unbalance and alleviate the voltage rise, and thus improve the HC of the unbalanced three-phase DNs. Considering that locations and capacities of distributed rooftop PVs are determined by customers, future PV installations are regarded as uncertainties. In addition, to deal with the uncertainties, the proposed BESS allocation problem is formulated as an adaptive robust optimization (ARO) model with integer recourse variables. Accordingly, a solution algorithm which integrates an alternating optimization procedure into a column-and-constraint generation algorithm is developed to efficiently solve the ARO model. With the proposed BESS allocation method, a new perspective on HC improvement is provided, which not only considers the worst power unbalance situation but also satisfies the allowed maximum PV capacity. The simulation results verify high efficiency and solution robustness of the proposed allocation method. [ABSTRACT FROM AUTHOR]

The high penetration of volatile renewable energy challenges power system operation. Energy storage units (ESUs) can shift the demand over time and compensate real-time discrepancy between generation and demand, and thus improve system operation flexibility and reduce renewable energy curtailment. This paper proposes two parametric optimization models to quantify how the power (MW) and energy (MWh) capacity of ESU would impact renewable energy utilization from two aspects: renewable energy curtailment and system flexibility for uncertainty mitigation. The two indicators are characterized as multivariate functions in the capacity parameters of ESUs. A severity ranking algorithm is suggested to pick up critical scenarios of fluctuation patterns from the uncertainty set; consequently, the proposed models come down to multi-parametric mixed-integer linear programs (mp-MILPs) which can be solved by a decomposition algorithm. The proposed method provides analytical expressions of the two indicators as functions in MW and MWh capacity. Such a characterization delivers abundant sensitivity information on the impact of ESU capacity parameters, and provides a powerful tool for visualization and useful reference for storage sizing. Case studies verify the effectiveness of the proposed method and demonstrate how to use the geometric information. [ABSTRACT FROM AUTHOR]

This paper proposes a novel nested restoration decision system (NRDS), which aims to minimize unused capacity of distributed generation (DG) for service restoration due to contingency during islanding stage. The proposed algorithm is based on the concept of nested microgrid formation, where the network control schemes are framed on a layered structure. The power-sharing strategy between neighboring microgrids in the network is from outer to inner microgrid loads, where the outermost microgrid exchanges power with utility grid only. The first stage refers to pre-fault scenario, which finds a solution for networked microgrid distributed generation as the initial setting. Furthermore, the second stage calculates additional redistribution requirements based on respective microgrid's deficiency using a solution index matrix. The proposed control has been evaluated on a networked microgrid system, while performing islanding. [ABSTRACT FROM AUTHOR]

Deploying the cloud energy storage system (CESS) is an economic and efficient way to store excess photovoltaic generation and participate in demand response without personal investment on pricy energy storage equipment. It is a shared battery energy storage system (BESS) for local residential and small commercial consumers, which is designed and controlled by the CESS operator. Based on the profit purpose, the CESS operator not only pursues the most economic operating strategy, but also tries to minimize the total investment on the design stage. This paper considers the investment on the batteries, power conversion system, reactive power compensation equipment and the cost including battery degradation cost and operation cost. The electricity price uncertainty and the voltage deviation of the CESS node caused by power exchange are also considered. Moreover, the cases of a largely centralized energy storage system and multiple distributed energy storage systems are all modelled. Finally, an original robust cooptimization model is transferred to a mixed integer linear programming model (MILP) and solved in GAMS. Numerical results based on historical data from 300 residential consumers in Australia present that the battery degradation cost and price uncertainty can't be neglected. [ABSTRACT FROM AUTHOR]

With rapid growth of distributed renewable generation, the establishment of electricity distribution markets has attracted widespread concerns. Different from existing transmission grid-scale electricity markets, an electricity distribution market is featured by numerous small-scale prosumers, and zero marginal cost and intermittency of renewable generation units. Against this background, this paper first extends an average pricing market (APM) mechanism for pricing renewable generation outputs with zero marginal cost in the distribution network concerned. Then, to mitigate the uninstructed volatility of renewable generation outputs and power demand, a penalty scheme is proposed for deviations between the real-time demand/output and market cleared bid/offer, with frequency regulation service (FRS) from energy storage systems (ESSs) considered. It is proved that the market volatility can be well controlled within an expected limit through properly setting the penalty prices for load demand and generation output fluctuations. Also, with this mechanism a non-negative market surplus could always be attained. Case studies are carried out to demonstrate the feasibility and efficiency of the proposed distribution market mechanism and penalty scheme. [ABSTRACT FROM AUTHOR]

Notable benefit can be brought by combined operation of a coupled electricity and heat system (CEHS), and be enhanced by introducing thermal energy storage (TES). Existing literature researching on this topic either neglects the heat network, or requires TES system being located and operated together with a combined heat and power unit. This paper aims at removing such requirement whilst taking heat network into account. With prevalent constant-flow variable temperature control strategy of heat network, the optimal dispatch model of CEHS considering independent TES (ITES) system with networked electric and heat energy distribution systems is presented. The non-convex parts in the heat network, mainly caused by the MFR reversal when ITES changes its operation state, are exactly reformulated to mixed-integer linear (MIL) constraints, which leads to a mixed-integer second-order programming formulation of the problem. Case studies demonstrate that introducing ITES not only brings reduction in the total operation cost, but also ameliorates the system reliability and its capability in accommodating renewables. [ABSTRACT FROM AUTHOR]

The growth in residential air-conditioning is a primary contributor to electric utility critical peak load causing millions of dollars spent on extra network infrastructure to cater for these peak times. This paper aims to provide an attempt to coordinate multiple groups of aggregated air-conditioners for distribution network loading management. Through limited communication to exchange information among neighboring aggregators, the proposed dispatch strategy shares the required active power curtailment among aggregators, maintaining room temperatures to keep occupants comfort in the meanwhile. Three case studies and sensitivity analysis are conducted to show the performance of the proposed scheme. The results show that it can provide technical and economic benefits to both participating residents and network operators. [ABSTRACT FROM AUTHOR]

The smart distribution system architecture provides value‐based control techniques that facilitate bi‐directional power flows and energy transactions. Although consensus and understanding continue to develop around peer‐to‐peer transactions, a distribution system operator aims to promote and enable interoperability among entities, particularly those who own distributed energy resources such as energy storage system (ESS) and distributed generation (DG). In this study, the authors address the optimal allocation of ESS and DG in the smart distribution system architecture, in order to help the integration of wind energy. The formulated objective is to minimise the sum of the annualised investment cost, the expected profit and the imbalance cost in the two‐stage of power scheduling. The proposed model is verified on the modified IEEE 15‐bus distribution radial system. The simulation results have verified the proposed planning approach. Also, results show that a more risk‐seeking operation strategy is recommended if wind power penetration increases. [ABSTRACT FROM AUTHOR]

The growth in installed solar photovoltaic (PV) capacity and the ever-increasing power demand due to the use of energy-hungry appliances have caused voltage issues. In this paper, a hierarchical dispatch strategy is proposed for coordinating multiple groups of virtual energy storage systems (VESSs), i.e., residential houses with air conditioners, to regulate voltage in low-voltage (LV) grids with high solar PV penetration. Specifically, the two levels of the proposed model are: 1) in the lower level, VESSs within each intelligent residential district are controlled locally by individual aggregator; 2) in the upper level, multiple aggregators are coordinated to achieve voltage regulation through a consensus control strategy. By exchanging information through sparse communication links, each aggregator shares the required active power adjustment among all participating groups, without compromising users’ thermal comfort. Simulation result demonstrates that the proposed control scheme can effectively regulate voltage in LV grids with greater robustness and scalability. [ABSTRACT FROM AUTHOR]

This study presents a two‐stage planning framework of the battery energy storage system (BESS) and micro‐turbine (MT) in a microgrid. In the first stage, the optimal allocation decisions are made before the actual realisation of the operational uncertainties. In the second stage, the optimal operation strategies are made for the microgrid by minimising the costs paid to the main grid, fuel cells, MTs, BESSs and controllable loads (CLs). The hot water system and interruptible load are considered as CLs. Their mathematical models are built to investigate their roles in smoothing renewable energy. In addition, efforts are made to keep the linearity of the formulated optimisation problem, and the backward scenario reduction method is adopted to further enhance the computational efficiency. The modified IEEE 33‐bus radial system is used as a microgrid to verify the effectiveness of the proposed approach for both islanded and grid‐connected microgrids. Sensitivity analysis has been conducted to compare the economics and robustness of the identified solutions. [ABSTRACT FROM AUTHOR]

Being more actively involved in the electricity market and power systems, wind farms are urgently expected to have similar controllable behavior to conventional generations so that demand assigned by the system operator can be met. However, determining the method of dispatching the reference among the widely spread and low-rating wind turbines is difficult. This paper provides a cooperation-driven distributed control scheme for wind farm active power regulation. Instead of competing with neighboring controllers completely, the control strategy evaluates system-wide impacts of local control actions, and aims to achieve coordinated control effect. In addition, the kinetic energy storage potential in a wind turbine is tapped to provide a buffer for power dispatch. Case studies demonstrate that a large wind farm can be effectively controlled to accurately track the demand power through the proposed control scheme. [ABSTRACT FROM PUBLISHER]

This paper proposes a robust optimization approach for optimal operation of microgrids. The uncertain output variation of renewable energy sources (RESs) is addressed by collaboratively scheduling of energy storage (ES) and direct load control (DLC) through a two-stage complementary framework: an hour-ahead charging/discharging of ES and a quarter-hour-ahead activation of DLC. The objective is to maximize the total profit of the microgrid considering operation and maintenance costs of ES units, wind turbines and photovoltaics, and transaction with main grid and customer loads. Assuming the power output of RES randomly varies within a bounded uncertainty set, the problem is modeled to a two-stage robust optimization model and solved by a column-and-constraint generation algorithm. Compared with conventional operation methods, the ES and DLC are coordinated in different time-scales, and RES uncertainties are fully addressed during operation decision-making, ensuring the solutions to be optimal and robust for any realization of uncertainty. The proposed methodology is verified on the IEEE 33-bus distribution system through a wide range of different tests. [ABSTRACT FROM AUTHOR]

System overloading is becoming a critical issue in distribution system due to outdated infrastructure and growing electricity demand. Although renewable‐based distributed generation is a promising solution to relieve this issue, its intermittency and uncertainty impose significant challenges on system operations. This study attempts to coordinate networked energy storage systems (NESSs) to manage network loading in distribution networks. The NESS can act as a buffer to absorb surplus energy during high generation periods and serve the demand during peak load periods. A consensus‐based dispatch strategy is proposed to coordinate NESSs. Through limited communication among neighbouring NESSs, required active power curtailment is shared. The mathematical formulation of a state‐of‐charge weighting factor is introduced to improve the efficiency of NESSs. A sensitivity study is also conducted to demonstrate the performance of the proposed strategy. [ABSTRACT FROM AUTHOR]

Along with the increasing penetration of renewable energy, distribution system power flow may be significantly altered in terms of direction and magnitude. This will make delivering reliable power, on demand, a major challenge. In this paper, a novel battery energy storage system (BESS) based energy acquisition model is proposed for the operation of distribution companies (DISCOs) in regulating price or locational marginal price (LMP) mechanisms, while considering energy provision options within DISCO controlled areas. Based on this new model, a new battery operation strategy is proposed for better utilization of energy storage system (ESS) and mitigation operational risk from price volatility. Meanwhile, optimal sizing and siting decisions for BESS is obtained through a cost-benefit analysis method, which aims at maximizing the DISCO's profit from energy transactions, system planning and operation cost savings. The proposed energy acquisition model and ESS control strategy are verified on a modified IEEE 15-bus distribution network, and risk mitigation is also quantified in two different markets. The promising results show that the capacity requirement for ESS can be reduced and the operational risk can also be mitigated. [ABSTRACT FROM PUBLISHER]

In this letter, a distributed model predictive control strategy for battery energy storage systems is proposed to regulate voltage in distribution network with high-renewable penetration. Control actions are calculated based on communication between interconnected neighboring subsystems and a multistep receding optimization, also considering system and battery constraints. The proposed approach is shown to be highly effective through a simulation case study, indicating high potential for applications. [ABSTRACT FROM PUBLISHER]

Highlights • A two-layer collaborative planning for inverter interfaced DGs and BES units is established for enhanced voltage regulation functions in ADN. • The potential of DG inverter over-sizing design at planning stage, the fully utilization mode of reactive power output of DG inverters at operation stage are discussed. • A time-varying ZIP load model is adopted representing the dynamic load characteristics. • The uncertainties of the DG power output are modeled based on the Taguchi robust test design approach. Abstract Active Distribution Networks (ADNs) are featured by large-scale integration of distributed generation (DG) and energy storage. This paper proposes a novel two-layer co-planning method for optimal placement of inverter-interfaced DG and battery energy storage (BES) units towards enhanced voltage regulation functions in an ADN. The outer-later model determines the planning decision of DG units and the inverter sizing, location, and capacities of BES units, respectively; the inner-layer model corresponds to the operation decision which aims to optimally schedule the BES's charging/discharging and reactive power from inverters for voltage regulation support considering the conservation voltage reduction (CVR). The Taguchi's Orthogonal Array Testing (TOAT) is used to select a small number of scenarios to represent the DG power output uncertainties. The load is represented by a time-varying ZIP load model. The proposed model is tested on a modified IEEE 33-bus radial distribution system and the results to validate the effectiveness of the proposed method. [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 increasing penetration of wind power significantly affects the reliability of power systems due to its intrinsic intermittency. Wind generators participating in electricity markets will encounter operational risk (i.e. imbalance cost) under current trading mechanism. The imbalance cost arises from the service for mitigating supply-demand imbalance caused by inaccurate wind forecasts. In this paper, an insurance strategy is proposed to cover the possible imbalance cost that wind power producers may incur. First of all, a novel method based on Monte Carlo simulations is proposed to estimate insurance premiums. The impacts of insurance excesses on premiums are analyzed as well. Energy storage system (ESS) is then discussed as an alternative approach to balancing small wind power forecasting errors, whose loss claims would be blocked by insurance excesses. Finally, the ESS and insurance policy are combined together to mitigate the imbalance risks of trading wind power in real-time markets. With the proposed approach, the most economic power capacity of ESS can be determined under different excess scenarios. Case studies prove that the proposed ESS plus insurance strategy is a promising risk aversion approach for trading wind power in real-time electricity markets. [ABSTRACT FROM AUTHOR]

The concerns on energy security and environment protection have driven the need to produce, transform and utilize energy in a more efficient, clean and diversified way. Under this background, we design a paradigm for energy hubs that combine distributed energy supply/combined cooling heating and power (DES/CCHP), renewable energy and energy storage. These energy hubs are comprised of heating, cooling and power systems, and natural gas, power generation and photovoltaic (PV) are the primary energy sources. Also, we propose a paradigm and its operation model for regional multi-energy prosumers (RMEP) whose energy demands are served by interconnected energy hubs. The energy exchange in energy hub is based on the structure of energy buses including power bus, heating bus and cooling bus. These energy buses are interconnected as a ring heating/cooling network and a radial power grid to implement mutually complementary reserves of energy hubs. Moreover, the bi-directional energy flows between prosumer and the main grid are also analyzed. In addition, an optimal scheduling model for RMEP is proposed. The formulated objective of this model is to minimize prosumer’s cost of purchasing electricity and natural gas plus the cost of GHG emission or to maximize the revenue of selling electricity back to the grid, while considering various electricity and gas prices and heating/cooling demands during different time periods. The decision variables for prosumer include the amount of purchased gas, the amounts of purchased and sold electricity at energy hubs. Case studies are undertaken on the 15-node multi-energy prosumer system, where the system comprises three energy hubs. Prosumer’s operational strategies under system normal and contingency conditions on typical summer and winter load days can be obtained. Comparative analyses between mutually independent energy hubs are also conducted. According to the simulation results, prosumer can play an important role in responding to the time-of-use electricity and gas tariffs, shaving the regional peak loads as a whole. Besides, the interconnected energy hubs can enhance the overall system operational flexibility and reliability. [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]

• Review the state-of-the-art research works of integrated energy systems of data centers and smart grids. • Propose future integration scenarios for data centers and smart grids. • Analyze the challenges of implementing integrated energy systems of data centers and smart grids. Cloud computing platforms are critical cyber infrastructures in modern society. As the backbone of cloud systems, data centers act as large energy consumers in today's power grids. The integration of on-site renewable energy sources and energy storage systems further transforms data centers to be energy prosumers (producers-and-consumers). As a result, optimizing data centers' energy production and consumption, and exploiting their potential of actively engaging in the external grid's planning, operation, and control has been drawing increasing attention in the last few years. This paper conducts a comprehensive review of the state-of-the-art research efforts on integrated energy systems of data centers and smart grids. A taxonomy of such integration scenarios is provided. Consequently, this paper identifies several future application scenarios of integrating data centers and smart grids, which serves as a roadmap towards future research. This article is expected to provide a useful reference for researchers and engineers in the areas of energy systems and cloud computing. [ABSTRACT FROM AUTHOR]

• A sharing platform framework with energy and capacity sharing of hybrid energy storage system (HESS) is proposed. • Detailed technic-economic analysis has been conducted. • The characteristics of electrical and thermal demands are captured, in order to assist the energy management of HESS. • Flexibility from multi-energy systems studied is including electricity, heat and gas sector and energy storage. • Energy sharing and flexibility of end-users are also considered. There is already a large amount of energy storage system (ESS) and demand response potential in the power, heat and gas system, which can be used to promote a cost-effective transition to low-carbon and renewable energy. This paper proposes an energy sharing platform to effectively integrate power, thermal and gas systems of different sizes to balance the fluctuation of renewable energy and improve the system reliability. In a potential application in the hospitality industry, hotels can jointly share and rent ESS. This sharing platform uses a hybrid energy storage system (HESS), comprising BESS and thermal energy storage system (TESS). Unlike BESS, TESS is cost-effective and can be provided by hot water tanks as short-term energy storage. The capacity and energy sharing method of the hybrid BESS and TESS system is provided, including the detailed rental model of HESS in the operation economy. Moreover, the coupled energy supply chain is also considered, the combined utilization of thermal, gas and electricity sharing is achieved. The proposed model is evaluated on the IEEE 18-bus system with the real historical data in Australian energy market, and the cost-benefit analysis is also provided. [ABSTRACT FROM AUTHOR]