Energy Storage Planning Research Articles

Capacity expansion planning for wind power and energy storage considering hourly robust transmission constrained unit commitment

The installed capacity of renewable energy in power systems is rising rapidly in recent years due to environmental pressure. And as the main asset of mitigating renewable output fluctuations, energy storage (ES) also has been greatly developed with the increase of renewable capacity. To this end, the capacity planning of renewables and ESs has drawn much attention and many methods have been proposed. In the formulations of capacity planning problems, more detailed and complicated operation constraints mean more accurate planning results, which is the consensus of many researchers. However, to guarantee the problem formulation tractable, the actual multistage operation process of power system is not properly considered in existing planning methods. Therefore, in this paper, a new capacity expansion planning method for wind power and ESs is proposed considering the actual multistage operation process of power system. Specially, the hourly robust transmission constrained unit commitment (TCUC) and economic dispatch (ED) are involved in this planning method and thus, it could accurately evaluate the operational cost under certain planning decision. Besides, to guarantee the solvability and computational efficiency of planning method, a parallel horizon-splitting method is improved to solve the long-time-horizon hourly robust TCUC problem, and a genetic algorithm nested gradient descent method is established to accelerate the solving of planning problem. With the actual multistage operation process, a more reasonable expansion planning decision is obtained and the computational efficiency is greatly improved by the proposed acceleration algorithm. Numerical tests verify the efficacy of proposed method.

Joint planning of electrical storage and gas storage in power-gas distribution network considering high-penetration electric vehicle and gas vehicle

Increasing penetration of electric vehicles (EVs) and gas vehicles (GVs) will endanger safe and stable operation of power-gas distribution network. Energy storage systems are considered effective tools to deal with the surge of charging demands brought by EV/GV and enhance energy supply reliability. Meanwhile, the coupling between power and gas distribution systems has been strengthened in recent years via gas turbines. This makes it possible and imperative to jointly optimize the configuration of electrical and gas storage systems and avoid the overinvestment commonly occurring in separate planning. To this end, this paper proposes a joint electrical and gas energy storage planning approach considering the interdependency between power-gas distribution network and transportation network. First, the semi-dynamic traffic assignment method is utilized to obtain EV/GV traffic flow and its transition between two adjacent periods. EV/GV traffic flow is directly related to EV/GV charging demands and can be further converted into their spatial–temporal distribution. Second, a novel second-order cone formulation is proposed to accurately describe the nonlinear operating characteristic of gas storage system. This can help to incorporate the constraints associated with the effective operation of gas storage systems into the planning model, while ensuring the computational tractability. Finally, the proposed planning problem is formulated as a mixed-integer second-order conic programming problem. A penalty convex-concave procedure algorithm is developed to ensure the exactness of all second-order cone relaxations in our proposed model. Numerical results indicate that joint planning strategy can fulfill the peak charging load while yielding to low investment cost.

Integrated expansion planning of electric energy generation, transmission, and storage for handling high shares of wind and solar power generation

In this paper, an integrated multi-period model for long term expansion planning of electric energy transmission grid, power generation technologies, and energy storage devices is introduced. The proposed method gives the type, size and location of generation, transmission and storage devices to supply the electric load demand over the planning horizon. The sitting and sizing of Battery Energy Storage (BES) devices as flexible options is addressed to cover the intermittency of Renewable Energy Sources (RESs), mitigate lines congestion, and postpone the need for new transmission lines and power plants installation. For efficient handling of RESs uncertainties, and operational flexibility, the upward and downward Flexible Ramp Spinning Reserve (FRSR) are modeled. Besides, the Low-Carbon Policy (LCP) is considered in the objective function of the proposed Transmission, Generation, and Storage Expansion Planning (TGSEP) model. A hierarchical clustering method that can preserve the chronology of input time series throughout the planning horizon periods is developed to capture the short-term uncertainties of load demand and RESs. The short-term operational flexibility requirements make the joint long-term transmission and generation planning a high computational problem. Therefore, the Mixed-Integer Linear Programming (MILP) formulation of the model is solved using an accelerated Benders Dual Decomposition (BDD) method. The IEEE RTS test system is utilized to validate the effectiveness of the proposed joint expansion planning model.

Stochastic expansion planning of battery energy storage for the interconnected distribution and data networks

This paper presents a stochastic expansion planning framework to determine the installation time, location, and capacity of battery energy storage systems in the distribution networks with considerable penetration of photovoltaic generation and data centers. The presented framework aims to minimize the capital cost of the battery energy storage and the operation cost of the distribution network while ensuring the security of energy supply for the data centers that serve end-users in the data network as well as the reliability requirements of the distribution network. The proposed stochastic framework captures the interactions between the distribution network and data center operators considering limited shared information among these entities. Benders decomposition is used to capture the interactions between these autonomous operators in the electricity and data networks. The uncertainties associated with the electric demand, data center workload, solar PV generation, and the availability of the distribution branches are captured using Monte Carlo simulation. The representative scenarios are selected using a dissimilarity-based sparse subset selection algorithm. To evaluate the effectiveness of the proposed framework, numerical results are presented for a modified IEEE-34 bus distribution network with data centers and PV generation units.

An economy and reliability co-optimization planning method of adiabatic compressed air energy storage for urban integrated energy system

The improving energy supply reliability requirements pose significant challenges to the energy storage planning in the urban integrated energy system (UIES). Traditional energy storage planning methods usually only take the economy optimization as the objective. These methods have difficulty meeting the requirement of reliability. The co-optimization of economy and reliability has been regarded as a new target. However, this new target is difficult to be achieved because of the complexity of energy storage resources in UIES and the nonlinearity of mathematical formulation to express the reliability. Therefore, how to achieve the maximum comprehensive improvement of economy and reliability within the limited scope of planning resources is a key issue that needs to be resolved. Adiabatic compressed air energy storage (A-CAES) has the capability of combined cooling, heating and power supply. The incorporation of A-CAES in UIES can improve the system economy and reliability effectively. To this end, a reasonable bi-level optimal planning method for A-CAES combined with heat/cold energy storage is proposed considering the economy and reliability co-optimization. Firstly, the mathematical formulations of economy and reliability objectives are presented separately. To unify the two planning objects, the reliability object is transformed from the minimum outage capacity into the minimum outage penalty cost, which is regarded as the reliability cost. The reliability cost and economy cost are integrated as the objective function of the planning level. Then, based on the rated power and capacity of A-CAES obtained from the planning level, the optimal scheduling model of UIES is established for evaluating the annual system operation cost and reliability cost. Finally, a UIES containing an A-CAES is used for simulation analysis. The simulation results show that the proposed model can achieve the comprehensive optimization of economy and reliability.

A distributed computing framework for multi-stage stochastic planning of renewable power systems with energy storage as flexibility option

An integrated generation, transmission, and energy storage planning model accounting for short-term constraints and long-term uncertainty is proposed. The model allows to accurately quantify the value of flexibility options in renewable power systems by representing short-term operation through the unit commitment constraints. Long-term uncertainty is represented through a scenario tree. The resulting model is a large-scale multi-stage stochastic mixed-integer programming problem. To overcome the computational burden, a distributed computing framework based on the novel Column Generation and Sharing algorithm is proposed. The performance improvement of the proposed approach is demonstrated through study cases applied to the NREL 118-bus power system. The results confirm the added value of modeling short-term constraints and long-term uncertainty simultaneously. The computational case studies show that the proposed solution approach clearly outperforms the state of the art in terms of computational performance and accuracy. The proposed planning framework is used to assess the value of energy storage systems in the transition to a low-carbon power system.

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.

Optimal Coordinated Planning of Energy Storage and Tie-Lines to Boost Flexibility with High Wind Power Integration

Since renewable power is intermittent and uncertain, modern grid systems need to be more elegant to provide a reliable, affordable, and sustainable power supply. This paper introduces a robust optimal planning strategy to find the location and the size of an energy storage system (ESS) and feeders. It aims to accommodate the wind power energy integration to serve the future demand growth under uncertainties. The methodology was tested in the IEEE RTS-96 system and the simulation results demonstrate the effectiveness of the proposed optimal sizing strategy. The findings validate the improvements in the power system reliability and flexibility.

Trading-oriented battery energy storage planning for distribution market

In this paper, we present a trading-oriented battery energy storage system (BESS) planning model for a distribution market. The proposed planning model is formulated as a mutual-iteration and multi-objective two-stage optimization problem. The first stage is designed to optimize the internal resources allocation including PV system, wind turbine, gas turbine and BESS. Then a double-side auction model named as averaging pricing market (APM) mechanism is presented in the second stage, which aims at solving the issue of energy trading benefit loss caused by the relatively high difference between time-of-use (ToU) electricity price and feed-in-tariff. The contribution of this paper lies in combining the planning of BESS with the optimization of the distribution electricity market. Numerical results on a modified IEEE 24-bus system are presented to demonstrate the superiority of the two-stage optimization model and the sensitivity of the model is also investigated based on regret analysis.

Long-term Operational Planning of Energy Storage and Supply Systems Based on Time-Domain Decomposition

Long-term Operational Planning of Energy Storage and Supply Systems Based on Time-Domain Decomposition

Long-term operational planning of energy storage and supply systems based on time-domain decomposition

Long-term operational planning of energy storage and supply systems based on time-domain decomposition

Coordinated Planning of Electricity/gas/storage Distribution Network Based on LSTM and Demand Response

This paper presents a collaborative planning method of an electricity-gas-storage regional integrated energy system based on LSTM neural network and demand response. First, the LSTM Neural network is used for load forecasting, and the energy hub structure of the electric gas storage system is established. Then, the mathematical models of power storage, gas storage, electric network topology, gas network topology, and P2G are established to minimize the expansion cost of the electricity-gas-storage system, and the collaborative planning of energy storage, power lines, and natural gas pipelines is proposed based on the existing electric gas coupling integrated energy system. The original model which is difficult to solve is transformed into a mixed-integer linear programming model by introducing auxiliary variables, and the CPLEX solver is called to solve it. Finally, the economic advantages of collaborative planning of electricity-gas-storage system are verified by an example, and the connection of power storage and gas storage can reduce system pressure and optimize equipment selection.

EV Fleet as Virtual Battery Resource for Community Microgrid Energy Storage Planning

EV Fleet as Virtual Battery Resource for Community Microgrid Energy Storage Planning

Optimal planning and investment benefit analysis of shared energy storage for electricity retailers

With the rapid development of energy storage (ES) technology, it has gradually become a vital facility to cope with the intermittent renewable generation and reduce the users’ electricity purchase cost. However, the limited application of the ES has suffered from its high capital cost. This paper proposes an approach of optimal planning the shared energy storage based on cost-benefit analysis to minimize the electricity procurement cost of electricity retailers. First, the multi-time scale electricity purchase model is established. Then the retailers are screened and classified based on the proposed matching degree function to select the collective of retailers, which maximizes the profits of planning the shared ES. The life cycle cost model and the equivalent cycle life method are used to evaluate the benefit of investing the shared ES. The benefit distribution among the collective is conducted based on the contribution degree of each retailer. In the case study, the optimization results of the shared ES for high-matching and low-matching groups are compared in detail. The simulation results illustrate that the costs are reduced by 8.83% and 8.03% respectively for the two groups of electricity retailers by the proposed approach. Results also verify that ES can effectively reduce the cost of retailers, and high matching degree can be used as the selection criterion to obtain a greater benefit of the shared ES.

Energy storage in high renewable penetration power systems: Technologies, applications, supporting policies and suggestions

Integrating renewable energy is one of the most effective ways to achieve a low-carbon energy system. The high penetration of variable renewable energy, such as wind power and photovoltaic, increases the challenge of balancing the power system. Energy storage technology is regarded as one of the key technologies for balancing the intermittency of variable renewable energy to achieve high penetration. This study reviews the energy storage technology that can accommodate the high penetration of renewable energy. The basic energy storage technologies that can accommodate time-scale variation are reviewed first. The role of energy storage in the generation, transmission, distribution, and consumption for the high renewable energy penetration system is then analyzed. The supporting energy storage policies in the United States, the United Kingdom and China are summarized. Specific suggestions are proposed from the perspectives of technology, business and policy. This paper provides guidelines for planning energy storage to enable a high renewable penetration power system.

Hybrid AC/DC Transmission Expansion Planning Considering HVAC to HVDC Conversion Under Renewable Penetration

In this paper, a dynamic (i.e., multi-year) hybrid model is presented for Transmission Expansion Planning (TEP) utilizing the High Voltage Alternating Current (HVAC) and multi terminal Voltage Sourced Converter (VSC)-based High Voltage Direct Current (HVDC) alternatives. In addition to new HVAC and HVDC lines, the possibility of converting existing HVAC transmission lines to HVDC lines is considered in the proposed model. High shares of renewable resources are integrated into the proposed hybrid AC/DC TEP model. Due to the intermittency of renewable resources, the planning of large-scale Energy Storage (ES) devices is considered. In order to accurately estimate the total TEP costs and hence capturing the scenarios of load and renewable generation uncertainty, using a clustering approach, each year of the planning horizon is replaced with four representative days. The proposed model is formulated as a Mixed-Integer Linear Programming (MILP) problem. Using Benders Decomposition (BD) algorithm, the proposed model is decomposed into a Master investment problem to handle the decision variables, and Sub-problems to check the feasibility of master problem solution and optimize the operation and ES investment cost. Three test systems are used as case studies to demonstrate the effectiveness of the proposed hybrid AC/DC TEP model.

Information system and distributed storage planning of smart distribution network based on distributed structure

The high permeability of distributed energy and the continuous expansion of distribution network demand higher requirements for distribution network planning and construction. The distribution network with distributed structure will be the development trend of smart distribution network in the future, and the control and planning of distribution network for distributed architecture will become a new direction for the research of smart distribution network. Intelligent distribution network information system is an important foundation to support the operation and planning of intelligent distribution network. From the point of view of communication network construction, power grid construction, distributed energy storage planning and grid structure, distributed distribution network planning indicators are proposed. The application of multi-agent system theory to build intelligent distribution network collaborative control system, on one hand, can reduce the cost of system construction and maintenance, and improve the flexibility and stability of the system. On the other hand, due to the high efficiency and real-time of data acquisition, the allocation of distributed storage can be achieved in the planning stage of distribution network. After distributed storage connecting to the distribution network, the system can coordinate other equipment in the distribution network for active management and control, and optimize the operation status of the distribution network.

Coordination planning of wind farm, energy storage and transmission network with high-penetration renewable energy

With larger scale renewable energy integrated, high-penetration renewable energy is deemed as one of the most important characteristics in the future power system. However, the inevitable uncertainty of both source and demand has brought significant challenges to planners. Since discoordination planning schemes restrict the optimal operation, a novel and efficient co-planning methodology is urgent to be investigated. Thus, we propose an innovative co-planning model of wind farm, energy storage and transmission network, which successfully takes imbalanced power, unit ramp capacity and incentive mechanism for renewable energy into consideration. To facilitate the renewable consumption, flexible implementations comprising optimal transmission switching (OTS) and unit commitment (UC) are integrated to further optimize the network topology and achieve flexible startup/shutdown of conventional unit. Furthermore, a linear tie-line model designed for multi-regional planning problems has been constructed to capture the presumed limitations. Afterwards, a decentralized decomposition algorithm based on analytical target cascading (ATC) is modified to deal with the proposed model through a completely parallel mode. The modified IEEE 48-bus test system demonstrates the priority of our proposed co-planning model and the corresponding solution algorithm.

How to Select the Optimal Electrochemical Energy Storage Planning Program? A Hybrid MCDM Method

Electrochemical energy storage (EES) is a promising kind of energy storage and has developed rapidly in recent years in many countries. EES planning is an important topic that can impact the earnings of EES investors and sustainable industrial development. Current studies only consider the profit or cost of the EES planning program, without considering other economic criteria such as payback period and return on investment (ROI), which are also important for determining an optimal EES planning program. In this paper, a new hybrid multi-criteria decision-making (MCDM) method integrating the Bayesian best-worst method (BBWM), the entropy weighting approach, and grey cumulative prospect theory is proposed for the optimal EES planning program selection with the consideration of multiple economic criteria. The BBWM and entropy weighting approach are jointly employed for determining the weightings of criteria, and the grey cumulative prospect theory was utilized for the performance rankings of different EES planning programs. Five EES planning programs were selected for empirical analysis, including 9MW PbC battery EES, 2MW LiFePO lithium ion battery EES, 3MW LiFePO lithium ion battery EES, 2MW vanadium redox flow battery EES, and 3MW vanadium redox flow battery EES. The empirical results indicate the 2MW LiFePO lithium ion battery EES is the optimal one. The sensitivity analysis related to different risk preferences of decision-makers also shows the 2MW LiFePO lithium ion battery EES is always the optimal EES planning program. The proposed MCDM method for the optimal EES planning program selection in this paper is effective and robust, and can provide certain references for EES investors and decision-makers.

A Generic Algorithm for Planning the Year-Round Solar Energy Harvest/Storage to Supply Solar-Based Stable Power

AbstractAn algorithm and modeling are developed to make precise planning of year-round solar energy (SE) collection, storage, and redistribution to meet a decided demand of electrical power fully relying on solar energy. The model takes the past 10 years’ data of average and worst-case sky coverage (clouds fraction) condition of a location at a time interval (window) of per 6 min in every day to predict solar energy and electrical energy harvest. The electrical energy obtained from solar energy in sunny times must meet the instantaneous energy demand and also the need for energy storage for nighttime and overcast days, so that no single day will have a shortage of energy supply in the entire year and yearly cycles. The analysis can eventually determine a best starting date of operation, a least solar collection area, and a least energy storage capacity for cost-effectiveness of the system. The algorithm provides a fundamental tool for the design of a general renewable energy harvest and storage system for non-interrupted year-round power supply. As an example, the algorithm was applied for the authors’ local city, Tucson, Arizona of the U.S. for a steady power supply of 1 MW.