Abstract-A two-stage optimization method is proposed for optimal distributed generation (DG) planning considering the integration of energy storage in this paper. The first stage determines the installation locations and the initial capacity of DGs using the well-known loss sensitivity factor (LSF) approach, and the second stage identifies the optimal installation capacities of DGs to maximize the investment benefits and system voltage stability and to minimize line losses. In the second stage, the multi-objective ant lion optimizer (MOALO) is first applied to obtain the Pareto-optimal solutions, and then the 'best' compromise solution is identified by calculating the priority memberships of each solution via grey relation projection (GRP) method, while finally, in order to address the uncertain outputs of DGs, energy storage devices are installed whose maximum outputs are determined with the use of chance-constrained programming. The test results on the PG&E 69-bus distribution system demonstrate that the proposed method is superior to other current state-of-the-art approaches, and that the integration of energy storage makes the DGs operate at their pre-designed rated capacities with the probability of at least 60% which is novel. Index Terms--active distribution network; distributed generation planning; two-stage optimization; energy storage; chance-constrained programming; multi-objective ant lion optimizer. I. INTRODUCTION Nowadays, with the increasingly high penetration of renewable distributed generation (DG) sources, active distribution networks (ADNs) have been regarded as an important solution to achieve power system sustainability and energy supply security [1, 2] . Recently, it is becoming an inevitable trend to make full use of renewable DGs such as wind turbines (WT) and photovoltaic (PV) units, since they have substantial advantages, such as power loss reduction, greenhouse gas emission reduction, flexible voltage regulation, peak-load shaving, higher power quality, supply reliability enhancement [3, 4] , etc. However, extensive penetration of DGs greatly increases the risks of safe and economic operation of distribution networks since renewable DGs have inherently intermittent nature [5, 6] , which makes the planning more challenging than ever before. The traditional distribution network planning options, such as the addition or expansion of substations and lines, are unable to meet the needs of modern complex ADNs facing all alternatives together with generation and load uncertainties [6, 8, 9] . Therefore, it is necessary to deal with such key challenges in the issue of optimal DG placement. For this problem, many attempts have been made in the past two decades to solve it using different methodologies, including analytical approach [10] [11] [12] [13] , mixed integer non-linear programming (MINLP) [14] [15] [16] , Kalman filter algorithm [17] , and computational intelligence approach [18] [19] [20] . An analytical method was firstly introduced in [10] to find out the optimal placement of a single DG in both radial and meshed networks to minimize power losses. However, this approach only optimizes siting and considers DG sizing as fixed. In [11] , the locating and sizing of DGs are identified instantaneously using an analytical strategy. In [12] , an improved analytical approach for multiple DG planning was proposed for reducing energy losses, and the method was examined on three different test systems. A simple analytical type approach was recently presented to optimize the loss related to the active and reactive components of DG branch current. Different from the aforesaid works, a methodology based on based on MINLP was developed in [14] for optimally planning different types of renewable DGs for minimizing annual power loss. In [15] , same approach has been implemented for optimal renewable DG placement and sizing to improve the voltage stability margin. Considering the uncertainties of loads and DG outputs, a technique based on multi-objective MINLP was proposed for benefit maximization in distribution systems in [16] . In [17] , the optimal DG placement is determined via a Kalman filter algorithm to minimize the losses. More recently, computational intelligence techniques have been successfully adopted to deal with the DG planning issues. In [18] , a genetic algorithms-based expansion planning model considering DG integration and conventional
In REF , the ant-lion optimization heuristic was applied to distributed generation allocation to achieve optimal combinations of investment benefit, power losses, and voltage stability.
Optimal distributed generation planning in active distribution networks considering integration of energy storage
Applied Energy 210 (2018) 1073-1081
Engineering Mathematics