Planning Of Energy Storage Systems Research Articles

Providing a New Multiobjective Two-Layer Approach for Developing Service Restoration of a Smart Distribution System by Islanding of Faulty Area

One of the essential capabilities of a smart distribution network is to improve network restoration performance using the postfault islanding method. Islanding of the faulty area can be done offline and online. Online islanding will decrease load shedding and operation cost. In this study, a novel two-step mathematical method for system restoration after the fault is presented. A new mathematical model for the optimal arrangement of the system for the faulty area in the first layer is proposed. In this layer, the main objective is to decrease the distribution system’s load shedding and operational costs. In this regard, after the fault event, the boundary of the islanded MGs is determined. Then, in the second layer, the problem of unit commitment in the smart distribution network is addressed. In addition to the load shedding, optimal planning of energy storage systems (ESSs) and nondispatchable distributed generation (DG) resource rescheduling are also determined in this layer. The important advantages of the proposed approach are low execution time and operational costs. A demand response (DR) program has also been used for optimal system restoration. Solving the problem using the multiobjective method with the epsilon-constraint method is another goal of the paper, which simultaneously minimizes the cost and the emissions of the smart distribution network. The proposed model has been tested on an IEEE 33-bus system. Better performance of the proposed model compared to the techniques in the literature has been proven.

Optimal planning of energy storage system under the business model of cloud energy storage considering system inertia support and the electricity-heat coordination

As the penetration rate of renewable energy increases in the electric power system, the issues of renewable power curtailment and system inertia shortage become more severe. Innovative solutions such as Cloud Energy Storage (CES) can be employed to address this challenge. However, the energy storage resources aggregated by the traditional CES business model mainly concentrate on Electrical Energy Storage (EES), which is still limited and expensive. It necessitates the exploration of new approaches to enhance the flexibility and cost-effectiveness of energy storage utilization, in which using District Heating System (DHS) as an equivalent energy storage resource of the power system is an effective method. Therefore, this paper proposes an optimal planning strategy of energy storage system under the CES model considering inertia support and electricity-heat coordination. Firstly, the system components and business model of the CES are described, and the framework of energy storage planning problem from the perspective of CES operator is formulated. Then the evaluation methods of energy storage utilization demand from CES users are proposed, including the evaluation of the renewable power curtailment, system minimum inertia requirement, and the equivalent energy storage ability of DHS. Based on this evaluation results, a bi-layer optimal energy storage planning model for the CES operator is established, where the upper-layer model determines the installed capacity of lithium (Li-ion) battery station and the lower-layer model determines the optimal schedules of the CES system. The numerical tests based on the operation profile of a typical city in China are carried out to demonstrate the effectiveness of the proposed method. The simulation result shows that the annual profit of the CES system can be improved by 15.26% after installing the energy storage system whose capacity is determined by the proposed method.

Probabilistic Expansion Planning of Energy Storage Systems Considering the Effect of Cycle Life

Energy storage systems (ESSs) are the key elements to improve the operation of power systems. On the other hand, these elements challenge the power system planners. The difficulties arise as a result of the ESSs’ economic and technological features. The cycle life of ESSs is a critical aspect that influences the choices made during expansion planning processes. In this manuscript, we have focused on a new model for the expansion planning of ESSs considering the impacts of technical properties, such as the cycle life and depth of discharge. For this purpose, the proposed model consists of the hourly operation planning of ESSs in the sample days of year. A new indicator is proposed to determine the daily charging/discharging cycles of ESSs. The numerical results show the ability of the proposed model to determine the optimal technology and capacity of ESSs.

Research on the Access Planning of SOP and ESS in Distribution Network Based on SOCP-SSGA

This paper proposes a two-stage planning model for soft open point (SOP) and energy storage system (ESS) that considers the cost of faults in response to the current issue of SOP and ESS systems not considering the impact of SOP access on load transfer in the event of a fault in the distribution network. Firstly, considering the uncertainty of “PV-load”, typical scenarios of PV and load are constructed based on the clustering algorithm. Secondly, aiming at the economic performance of the distribution network and the capacity of PV access, a two-stage optimization model is established for the joint integration of SOP and ESS into the distribution network (normal and fault operation) under typical scenarios. The model is solved by using the second-order cone programming algorithm and steady-state genetic algorithm (SOCP-SSGA). Stage one involves planning for the integration capacity and location of SOP and ESS into the distribution network under each scenario within a period based on SOCP with the goal of minimizing economic costs. In stage two, the PV access capacity of the distribution network is optimized using SSGA with the goal of enhancing the PV accommodation capability. Finally, verification and analysis are conducted on an improved IEEE33 node system. The results show that when the system optimizes access to a group of SOP and ESS, the total economic cost is reduced by RMB 61,729 compared to random access, and the accessible PV capacity is increased by 0.5278 MW. Moreover, optimizing access to two sets of SOP and ESS can further reduce the total economic cost by RMB 107,048 compared to the optimized access group and increase accessible PV capacity by 1.5751 MW. Therefore, the proposed plan for SOP and ESS planning in this paper can significantly reduce the economic cost of distribution networks, enhance the absorption capacity of distributed photovoltaics, improve the voltage level of power grid operation, and, thereby, improve the economic and reliability of distribution network operation.

Stochastic trading of storage systems in short term electricity markets considering intraday demand response market

In recent years, the renewable energy sources are used in the power system for reducing environmental pollution. One of these renewable energy sources is wind farm that is used widely. However, they increase the uncertainty of the power system. To solve this problem, plenty researches have proposed several methods that have some disadvantages. This paper tries to decline the effects of uncertainty of wind turbines by using of the energy storage systems and optimal demand response programs. A stochastic planning of energy storage systems and wind generation are presented in the short term electricity markets considering the trading day-head and intraday market of demand response. In order to formulate an accurate model of various components of system, all needed constraints are considered. In this problem, wind turbines, energy storage systems, and demand response aggregator work coordinately that is called virtual power plant. For better comparison, two states consist of coordinated and uncoordinated working of all components are considered and assessed. The various scenarios for electricity market price, adjustment market price, imbalance energy price, and the generated power of wind turbine are considered. These scenarios are generated with hybrid neural network and coati optimization algorithm. Also, the iterative fast progressive Kantorovich method is used for scenario reduction. The results show the better response of the proposed method in optimal operation of virtual power plant.

A resilience-oriented optimal planning of energy storage systems in high renewable energy penetrated systems

Natural events having a low occurrence probability and high impacts, such as windstorms and earthquakes, pose a danger to the distribution networks' optimal performance. To increase network resiliency, several operational solutions are necessary. This paper shows how to restore prioritized loads while meeting topological and operational constraints using mixed-integer linear programming. The model presents a plan for enhancing the interconnection of renewable energy sources (RESs), stationary battery energy storage systems (SBESSs), and power electric vehicles parking lots (PEV-PLs), which are used in the distribution system (DS), to get the optimal planning under normal and resilient operation. The stochastic optimization technique is used to model the influence of upstream grid pricing uncertainty on the optimal scheduling of (DS). Other major uncertainties, such as wind power, photovoltaic (PV), and active/reactive power for different types of electrical loads, are also simulated using stochastic optimization. In addition, demand response programs (DRP) and interruptible loads are introduced to enhance the resilient operation of the DS. The suggested planning model is tested on the IEEE 33-bus benchmark test system. The results confirm the feasibility and effectiveness of the suggested model in normal and resilient operation and to assess its performance.

Collaborative planning of cyber physical distribution system considering the flexibility of data centers

With the development of energy transition and smart grid, the traditional distribution network has transformed into a cyber–physical distribution network (CPDS), which is multidimensional and heterogeneous. The increasingly abundant cyber and physical resources and deep coupling characteristics provide new challenges for distribution network planning. Based on a distribution network with multiple distributed resources and data centers, this paper proposes a cyber–physical collaborative planning method for a distribution network. First, according to the overall architecture of CPDS and the cross-layer control manner, the multiple distributed resources model and spatial–temporal migration model of data loads are established. Then, taking the minimal annual cost of distributed resources as the objective function, the collaborative planning of energy storage systems (ESSs) and corresponding smart terminal units (STUs), spatial–temporal migration strategy of data load, and communication network topology is carried out. Finally, the economics and feasibility of the proposed planning scheme are verified by the modified IEEE-33 bus distribution system. The results show that the collaborative planning method proposed in this paper can reduce the investment cost of distribution network planning and solve the voltage variation problem to a certain extent, so as to improve the economy and operational reliability of distribution network, and provide theoretical guidance for planners.

Optimal Planning of Hybrid Electricity–Hydrogen Energy Storage System Considering Demand Response

In recent years, the stability of the distribution network has declined due to the large proportion of the uses of distributed generation (DG) with the continuous development of renewable energy power generation technology. Meanwhile, the traditional distribution network operation mode cannot keep the balance of the source and load. The operation mode of the active distribution network (ADN) can effectively reduce the decline in operation stability caused by the high proportion of DG. Therefore, this work proposes a bi-layer model for the planning of the electricity–hydrogen hybrid energy storage system (ESS) considering demand response (DR) for ADN. The upper layer takes the minimum load fluctuation, maximum user purchase cost satisfaction, and user comfort as the goals. Based on the electricity price elasticity matrix model, the optimal electricity price formulation strategy is obtained for the lower ESS planning. In the lower layer, the optimal ESS planning scheme is obtained with the minimum life cycle cost (LCC) of ESS, the voltage fluctuation of ADN, and the load fluctuation as the objectives. Finally, the MOPSO algorithm is used to test the model, and the correctness of the proposed method is verified by the extended IEEE-33 node test system. The simulation results show that the fluctuation in the voltage and load is reduced by 62.13% and 37.06%, respectively.

Storage Allocation in Active Distribution Networks Considering Life Cycle and Uncertainty

The modern active distribution systems necessitate integrating storage systems, thereby facilitating the large-scale proliferation of photovoltaic (PV) energy resources. This further calls for the optimal planning of energy storage systems, satisfying all the operational and economic constraints. This article describes an exhaustive storage integration method, deeming the life cycle of the battery energy storage, the uncertainty of load and PV output, and the islanded mode of operation of the system. A two-stage mixed-integer linear programming problem is formulated that determines the capacity and the number of discharge cycles of the batteries in the first stage. The lifetime of the battery based on the partial depth of discharge is analyzed in the second stage. Furthermore, the uncertainty and variability of PV and demand are taken into account through probabilistic analysis and time-period clustering. The method is validated on a standard 33-bus radial distribution network for the allocation of distributed lithium-ion batteries. Also, the method’s scalability is validated on a practical Indian distribution network and a 141-bus distribution network of metropolitan area of Caracas with distributed PV installations on various nodes.

A Supervised-Learning Assisted Computation Method for Power System Planning

To achieve a low-carbon economy, the comprehensive planning of the early retirement of coal-fired power plants (CFPPs), the construction of renewable energy plants, and the investment in energy storage systems (ESSs) are critical. However, the nonlinear characteristic of the alternating current power flow (ACPF) brings difficulties in solving the long-term planning problem efficiently. Hence, in this article, a learning and optimization integrated framework is presented to help the power sector to reach the emission reduction goal economically while guaranteeing system security and reliability. First, the electricity network transition from the fossil fuel-dominated system to the low-carbon-oriented system is planned. Then, a data-driven method is utilized to regress the power flows, and a method is proposed to integrate the data-driven model into an optimization problem to alleviate the heavy computational burden. Because the penetration of intermittent renewable energy is high in a low-carbon energy system, the security and reliability of the planning decisions are verified in the second level by the N-1 security check. Simulation results reveal that the data-driven method can calculate power flows more accurately than direct current power flow (DCPF) and linearized ACPF models. Also, the supervised-learning method can help reduce computing time. The proposed planning model is verified on the IEEE 30-bus system. Through case studies, it can be concluded that our proposed method can reach the low-carbon goals with the lowest cost, and reliability can be ensured at the same time. Because of the coordinated retirement of CFPPs and the proper planning of ESS, the planned electricity system can cope with uncertainties better.

A Review on Energy Storage Systems Planning in Active Distribution Networks and its Applications

A Review on Energy Storage Systems Planning in Active Distribution Networks and its Applications

Optimal planning energy storage for promoting renewable power consumption in the urgent situation of UHV systems

AC/DC hybrid ultra-high voltage (UHV) transmission network is an effective way to deliver large scale renewable energy. Unfortunately, the power transmission capacity is significantly restricted due to guaranteed transient stability. Energy storage systems (ESS) are regarded to be the most flexible means to enhance transient stability. However, optimal planning of ESS for UHV stability is challenge because it involves differential equations. For this, this paper firstly proposes the mathematic formulations for optimal planning of ESS with UHV transient stability. The proposed model considers the DC blocking fault that is simulated by component switching equivalent processing. Mathematically, the proposed model belongs to the nonlinear, mix-integer, discontinuous differential constrained programming. The existing simulation and numerical methods are challenging to solve the proposed model directly. For solving this problem, an effective method is proposed, combining with the methods of implicit trapezoidal integral method, sensitivity analysis method and interior point method. The simulation results show that ESS can enhance the transmission efficiency of UHV transmission lines. From the analysis of delivery capacity, voltage distribution, the generator rotor relative swing angle and angular velocity, the technical benefit can be maximized by optimal planning ESS with the proposed method.

Optimal energy storage planning for stacked benefits in power distribution network

Energy storage system (ESS) is regarded as an effective tool to promote energy utilization efficiency and deal with the operational risk of the power distribution network (PDN), which is caused by the inherent uncertainties of distributed energy resources and the surging of new loads from emerging energy sectors. Multiple benefits could be accrued by ESSs when providing various services to the PDN. However, investing in ESSs without a comprehensive cost-benefit analysis will underestimate the ESSs’ profitability and result in a suboptimal planning scheme. To this end, this paper proposes a bi-level ESS planning method considering the stacked benefits of ESSs. First, to facilitate the comprehensive cost-benefit analysis in the investment decision making process, systematic definitions and quantitative methods of ESS benefits are proposed to evaluate both the investment and operational benefits of ESSs properly. Novel ESS reserve model and load hosting capacity evaluation model are also developed to improve the accuracy of the benefit assessment. Second, a game theory based bi-level programming method is proposed to precisely capture the two-layer dynamic interaction structure of ESS planning using an easy-to-implement mathematical optimization-based formulation with global optimum guarantee. Specifically, the upper level optimizes the sites and sizes of ESSs for maximizing the stacked benefits, while the lower level economic dispatch model determines the optimal scheduling of the installed ESSs. Finally, to make the bi-level ESS planning problem tractable, it is reformulated as a single-level mathematical programming with equilibrium constraints by the duality-based reformulation. The linearization method is applied to further convert it into a mixed-integer programming problem that could be directly solved by commercial solvers. Numerical results verify that the proposed planning strategy could yield cost-effective results under different settings. Comprehensive cost-benefit analysis also indicates that transmission and distribution system upgrade deferral, reserve/regulation provision and energy arbitrage are the top-three applications for maximizing ESS benefits, and it is not always reasonable to keep investing in ESSs as their marginal benefits will decrease to zero with the increasing ESS investment.

Storage-Transmission Joint Planning Method to Deal with Insufficient Flexibility and Transmission Congestion

The insufficient power system flexibility and transmission congestion are two fundamental reasons for wind power curtailment. As the scale of the wind power in the power system is growing rapidly, the two factors of wind power curtailment events coexist and have a certain coupling relationship. The configuration of the energy storage system can not only increase the flexibility of the system but also alleviate transmission congestion. Therefore, the joint planning of energy storage and transmission grid that takes into account the flexibility of the system and the transmission congestion is of great significance to solve the wind curtailment. Hence, this paper first decouples the insufficient flexibility and transmission congestion wind power curtailment, and quantitatively analyzes the impact of transmission capacity on the coupling relationship between the two; second, reveals the principle of joint planning of energy storage system and transmission congestion, and constructs an optimization model, and proposes to set up the capacity of the wind power which connects the power network. The solution procedure, which deals with grid planning and the energy storage system optimization in turn, not only ensures the accuracy of the model, but also significantly reduces the calculation cost. Finally, a case study of a wind power base in Northeast China and an improved Garver-6 system are carried out to verify the effectiveness of the proposed method.

Enhancing the Resilience of Operational Microgrids Through a Two-Stage Scheduling Strategy Considering the Impact of Uncertainties

This article deals with the stochastic scheduling of a microgrid (MG) to balance the economical and resilience metrics. In the proposed model, the MG resilience indices are integrated into the economic criteria to ensure the resilience of MG operation alongside the main MG actors’ profit/loss. The MG fragility index, recovery efficiency index, MG voltage index, and lost load index are considered in the proposed planning model. Further, to make the results more realistic, the uncertainties associated with energy price and wind production, alongside with planning of energy storage systems and electric vehicles parking lots are considered. To achieve a better solution for the security-constraint operation of MG, AC network equations are included in the system modeling. Finally, a large-scale MG based on the IEEE-33 bus testbed is utilized to evaluate the effectiveness of the proposed stochastic scheduling program.

Bi-level optimal planning model for energy storage systems in a virtual power plant

Determining the optimal location and capacity of energy storage systems (ESS) is a crucial planning problem for the virtual power plant (VPP). However, the trading characteristics of VPP have not been considered in the existing ESS optimal planning research due to its complicated mathematical relationships. In this paper, considering the VPP trading environment, a bi-level optimization model is established to determine ESS’s optimal location and capacity. The proposed model is a mixed-integer, nonlinear, multi-period, and large-scale optimal problem, which is difficult to solve directly by traditional methods. Usually, linearizing or decomposing methods are used to simplify the original problem for solving. However, the available simplification methods greatly change the original problem, resulting in a large deviation between the simplified and the original problems. This paper proposes a novel interactive solution framework for the bi-level model based on the decomposition-coordination algorithm for improving the solution accuracy. The simulation results show the effectiveness of the ESS planning scheme in VPP under different objectives, EVs types, and electricity prices. Further analyses based on the IEEE 17-bus VPP system demonstrate that the proposed method can save nearly 29.01%, 28.08%, 26.62% of the ESS cost on average compared with the loss sensitivity factor, MILP-Based, and Benders decomposition methods, respectively.

Optimal sizing and placement of energy storage system in power grids: A state-of-the-art one-stop handbook

• Critical review on ESS planning covering ESS selection, modelling and algorithms. • Thorough discussion on requirements of ESS techniques in different sub-systems. • Systematic methodology of problem formulation for optimal ESS planning. • Comparison of algorithms focusing on optimization and uncertainty management. • Research directions on ESS planning considering demands in future grid. Energy storage system (ESS) has been expected to be a viable solution which can provide diverse benefits to different power system stakeholders, including generation side, transmission network (TN), distribution network (DN) and off-grid microgrid. Prudent ESS allocation in power grids determines satisfactory performance of ESS applications. Optimal sizing and placement of ESS are crucial for power quality improvement of DN and transmission system protection setting. To solve this issue, considerable researches have been done either in modelling or algorithms. However, various options and complex characteristics in different sub-systems make it difficult to appraise a specific method for an ESS application, while the existing reviews only focus on ESS applications in DN. This paper provides a critical one-stop handbook related to one hundred and four methods in six categories covering all kinds of networks and tailored applications. Meanwhile, a systematic methodology is presented with an extensive latest literature review, including ESS selection, evaluation criteria, modelling and solution methods. Particularly, different technical requirements and modelling methods of different sub-systems are outlined. Besides, pros and cons of optimization methods are thoroughly analysed and compared to reveal state-of-the-art studies. Finally, key points in optimal ESS sizing and placement are concluded along with recommendations for future research.

Dispatch-aware planning of energy storage systems in active distribution network

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.

Data-driven stochastic programming for energy storage system planning in high PV-penetrated distribution network

Energy storage systems (ESSs) facilitate the reliable and economic operation of distribution systems with high PV penetration. Establishing uncertainty models is the key to the optimal planning and operation of ESSs in distribution systems. Widely used parametric models cannot describe the variability of uncertainties thoroughly. In this paper, a data-driven method is designed for uncertainty modeling, and a distributionally robust optimization (DRO) model is developed to determine the optimal ESS planning strategy in distribution systems. First, a deterministic optimization model is established considering both ESS planning and distribution system operation. Then, the Wasserstein-metric-based ambiguity set is designed for the probability distributions of random variables. To hedge against the distributional ambiguity, the optimization problem is expanded into a two-stage DRO problem, of which the second-stage problem minimizes the expected operating cost under the worst-case probability distribution. Finally, the two-stage DRO model is reformulated as a mixed-integer second-order cone programming (MISOCP) problem, which is solved by optimization solvers. The proposed method is tested on modified 33-bus and 123-bus distribution systems with actual solar irradiance and load data. The influence of different strategies on ESS planning and operation is discussed. The ESS planning results obtained by DRO are compared with those of conventional stochastic optimization and deterministic optimization to verify the superiority of the proposed method.

Two-Layer Optimization Model for the Siting and Sizing of Energy Storage Systems in Distribution Networks

One of the most important issues that must be taken into consideration during the planning of energy storage systems (ESSs) is improving distribution network economy, reliability, and stability. This paper presents a two-layer optimization model to determine the optimal siting and sizing of ESSs in the distribution network and their best compromise between the real power loss, voltage stability margin, and the application cost of ESSs. Thereinto, an improved bat algorithm based on non-dominated sorting (NSIBA), as an outer layer optimization model, is employed to obtain the Pareto optimal solution set to offer a group of feasible plans for an internal optimization model. According to these feasible plans, the method of fuzzy entropy weight of vague set, as an internal optimization model, is applied to obtain the synthetic priority of Pareto solutions for planning the optimal siting and sizing of ESSs. By this means, the adopted fuzzy entropy weight method is used to obtain the objective function’s weights and vague set method to choose the solution of planning ESSs’ optimal siting and sizing. The proposed method is tested on a real 26-bus distribution system, and the results prove that the proposed method exhibits higher capability and efficiency in finding optimum solutions.