Distributed generation planning in active distribution network considering demand side management and network reconfiguration

Highlights

• A distributed generation planning model in active distribution network is proposed.

• Demand side management and network reconfiguration are taken into consideration.

• The proposed planning model is converted to a three-layer programming model.

• A hybrid solving strategy is developed to solve the model.

• The total cost over the planning horizon can be reduced 3.8% under the new model.

Abstract

This paper proposes a novel distributed generation (DG) planning methodology in active distribution network considering both demand side management and network reconfiguration. The objective function of the planning model is to minimize the total cost over the planning horizon, including investment cost of DG, operation and management cost of DG, fuel cost of DG, active management cost of DG, and demand side management cost. The constraints contain not only traditional DG investment and electrical restrictions (for instance, limitation of DG penetration, constraint of nodal voltage, constraint of branch capacity, etc.), but also the various restrictions of active management measures including regulating the on-load tap changer of transformer, controlling the output power of DG, demand side management and network reconfiguration. It is a large-scale mixed integer nonlinear programming model, which cannot be effectively solved by a single algorithm. Based on the idea of decomposition and coordination, the planning model is converted to a three-layer programming model. A hybrid solving strategy is developed to solve the model, in which differential evolution algorithm is used to determine the type, location and capacity of DG, and tree structure encoding-partheno genetic algorithm and primal–dual interior point method are applied to simulate the operation of active distribution network and find out the optimal operation state for each scenario. Case studies are carried out on a 61-bus active distribution network in East China, and results show that the total cost over the planning horizon can be reduced about 3.8% when demand side management and network reconfiguration are considered.

Introduction

With the rapid development of smart grid technology, active distribution network (ADN) is emerging in modern power system [1], [2]. In ADN, the devices can be real-time controlled and managed according to the operation demand in different scenarios. ADN is regarded as an important solution to consume distributed generation (DG) while keep the security and reliability of power supply [3]. When planning DG in ADN, not only the type, location and capacity of DG should be determined, but also the operation state of ADN should be simulated [4], [5].

Several studies have been reported on the optimal planning of DG in ADN, and each study has its unique characteristics. Refs. [6], [7], [8], [9] present the DG planning models with the objective function of minimizing energy loss, maximizing the penetration capacity of DG, maximizing the exploitation of DG, and minimizing the annual carbon emission, respectively. In these studies, the planning results are compared under different combinations of active management measures. These methods can make full use of the potential benefits of active management measures in the planning stage. Based on the theory of bi-level programming, Ref. [10] proposes a DG planning model with the objective function of maximizing the net benefit of the DG owner, and plant growth simulation algorithm and probabilistic optimal power flow are applied to solve the model. This planning method can handle the uncertainties and simulate the active management measures in an efficient way. In [11], an allocation model of renewable photovoltaic-thermal combined heat and power is presented with the objective function of maximizing the yearly economic profit of distribution companies. To solve the model, an efficient hybrid shuffled frog leaping algorithm is developed, which has higher computing accuracy and speed when compared to other evolution algorithms. In [12], a fuzzy multi-objective DG planning model is proposed, and imperial competitive algorithm is applied to solve the model. The characteristic of this planning method is to consider the economics, reliability, and environmental emissions simultaneously. Ref. [13] presents a DG planning method based on the hierarchical clustering analysis and differential evolution algorithm, the advantage of this method is to obtain the optimal planning scheme with reduced computational efforts. In [14], an optimal allocation method of renewable DG is proposed to maximize the profit of power distribution companies. The unique capability of this method is to integrate the independent power production and self-generation into distribution network without relying on predefined rules. Reference [15] presents a comprehensive teaching learning-based optimization technique for the optimal allocation of DG in distribution network to improve network loss reduction, voltage profile and annual energy savings. The proposed technique is parameter independent, and it can handle mixed integer variables and avoid falling into a local optimum. In [16], a multi-objective DG planning model is proposed, and a set of Pareto solutions are found by non-dominated sorting genetic algorithm II. Its advantage is to permit the decision-maker to make a trade-off between the investment cost of DG and power losses. In [17], an optimal planning and operation method of DG with market participation is reported, in which the energy transaction can be well considered. References [18] presents the joint expansion planning method of DG and distribution network considering active management measures, in which DG installation and network expansion can be co-optimized.

All the studies in references [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18] have helped a lot in the field of DG planning in ADN. However, demand side management (DSM) and network reconfiguration (NR) have not been considered in these studies. Attempts in Refs. [19], [20] are made to take into account DSM or NR when planning DG in ADN. Ref. [19] proposes a DG planning model in ADN considering NR, and the model is converted to a second-order cone programming model, which is solved by commercial solver GUROBI. But the planning model is simplified and has not taken into account DSM. In [20], a DG planning method considering DSM is presented to minimize the annual total cost, but the impact of NR is neglected.

To the authors’ best knowledge, the optimal DG planning method in ADN considering DSM and NR simultaneously has not been reported yet. To fill this gap, this paper incorporates both DSM and NR to develop a novel DG planning methodology in ADN. The main contributions of this paper are summarized as follows: (1) an optimal DG planning model in ADN is proposed which can take into account both DSM and NR, and it is converted to a three-layer programming model; (2) a hybrid solving strategy, combining differential evolution algorithm, tree structure encoding-partheno genetic algorithm, and primal-dual interior point method, is developed to solve the model.

The remaining sections of this paper are structured as follows. Section 2 briefly describes the concept of active management in ADN. Section 3 presents the mathematical formulation of the planning model. Section 4 depicts the hybrid solving strategy. Section 5 presents the case studies. Finally, Section 6 concludes this paper.