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

Highlights

• 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.

Abstract

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.

Introduction

There is a universally increasing demand for electricity and environmental protection, in parallel with global population booming, economic growth, technological advancement, and a series of environmental problems threatening human life [1,2]. Global electricity supply has been transformed by the ever-increasing share of renewable energy systems (RESs) due to their relatively low marginal cost and low emission merits [3,4]. However, RESs such as wind [5], [6], [7], [8] and solar power [9], [10], [11], [12] are intermittent in their nature, and the integration of significant amount of RESs into power grids poses crucial challenge on system operation [13], such as line congestion [14], energy deficits or surplus [15], voltage violation [16], RESs curtailment [17] and so on.

Energy storage system (ESS) is regarded as a viable solution for an affordable, reliable and sustainable power grid with large integration of RESs, including energy arbitrage [18], stability enhancement [19], congestion alleviation [20], generation efficiency improvement, loss reduction and gas emission reduction [21]. Besides, ESS can provide an effective supplement for RESs in smoothing output fluctuations [22]. A typical configuration of power grid with support of ESS in generation side, transmission side and distribution side, is shown in Fig. 1.

In addition to efficient operation and control strategies [23], [24], [25], [26], [27], [28], prudent ESS sizing and placement in power grids are crucial for stimulating the potential of ESS. Generally, insufficient sizing/ misplacing would degrade power quality or reliability and affect voltage and frequency regulation [29], while oversizing ESS may result in a larger financial burden and protective system misoperation. Therefore, considerable researches have been done to determine optimal sizing and placement of ESS.

In problem modelling, overwhelming majority of optimization models aim at achieving an excellent cost-effectiveness of ESS. For instance, reference [30] performed an elaborate cost-benefit model for optimal ESS sizing with minimal cost in a stand-alone hybrid system. Work [31] proposed an optimal ESS scheduling to maximize expected profit of a wind power plant by performing an effective utilization of wind power. Moreover, there are remarkable research efforts focusing on joint technical-economic model, where the cost of ESS implementation is minimized while still achieving holistic voltage improvement [32], RESs uncertainty alleviation [33], network losses reduction [34]. Meanwhile, numerous feasible methods are developed in order to properly perform optimal ESS sizing and placement. For example, literature [35] used a multi-period optimal power flow (OPF) to formulate ESS sizing problem in the form of single multi-scenario, which is however computationally intractable. In contrast, meta-heuristic algorithm (MhA) represented by genetic algorithm (GA) [36] and particle swarm optimization (PSO) [37], tend to offer higher efficiency and stronger flexibility. Furthermore, hybridization of different algorithms e.g., hybrid Mont Carlo-PSO [38], delivered promising results owing to achieve a better trade-off between performance enhancement and cost-effectiveness improvement of ESS.

Thus far, a large number of reviews have been undertaken in the context of ESS sizing and placement [29,[39], [40], [41]]. However, most of them focus on RES power plants and distribution network (DN). There is no doubt that transmission network (TN) and off-grid microgrid can also benefit from ESS, together with the fact that requirements and characteristics for different technical supports provided by ESS vary considerably in different sub-systems. Therefore, a comprehensive review covering all kinds of networks and all tailored applications should be provided to help understand the existing researches and the potentials in future studies related to ESS applications in power grid.

To close this gap, this paper serves to provide a comprehensive review of the state-of-art ESS sizing and placement methods. The general execution procedure of such problem is depicted in Fig. 2, while the following three steps are outlined:

• Step 1: Identify sub-system types (RES power plants, TN, DN or off-grid microgrid), where placed ESS determine the expected technical supports provided by ESS (i.e., applications). Then, select suitable ESS based on the characteristics of different technique options against the requirements of applications;

• Step 2: Formulate the problem based on the predetermined applications and selected ESS. Various economic and technical criteria are summarized in Section 3 to assess the performance of ESS applications, which can be taken as objective functions or constraints;

• Step 3: Solve the problem by appropriate methods. In Section 4, one hundred and four methods are summarized and classified into six typical categories. Their basic principles, flowcharts, pros and cons are described and tabulated to thoroughly reveal the current research trends.

The last section concludes this paper, along with some insights into future works. This paper can help to guide readers utilize these methods in a more effective manner and facilitate future research.