This paper deals with distribution network (DN) reconfiguration for loss minimization. To solve this combinatorial problem, a genetic algorithm (GA) is considered. In order to enhance its ability to explore the solution space, efficient genetic operators are developed. After a survey of the existing DN topology description methods, a theoretical approach based on the graph and matroid theories (graphic matroid in particular) is considered. These concepts are used in order to propose new intelligent and effective GA operators for efficient mutation and crossover well dedicated to the DN reconfiguration problem. All resulting individuals after GA operators are claimed to be feasible (radial) configurations. Moreover, the presented approach is valid for planar or nonplanar DN graph topologies and avoids tedious mesh checks for the topology constraint validation. The proposed method is finally compared to some previous topology coding techniques used by other authors. The results show smaller or at least equal power losses with considerably less computation effort.

A microgrid (MG) is a cyber-physical system with coupled power and communication networks. The centralized secondary control of MGs with periodical communications restricts system efficiency and resilience. This article proposes a distributed event-triggered secondary control scheme in islanded MGs with its cyber-physical implementation. The proposed control scheme operates with the reduced frequency of communications depending on the MG states change “events” (e.g., load variations and communication failures). Besides, the secondary control objectives, including frequency/voltage regulation and accurate real/reactive power sharing, are decoupled into two timescales. Instead of designing event-triggering conditions (ETCs) for each secondary control functions, only ETCs for power sharing control in slower timescale are designed. Thus, the communication burden is significantly reduced since communications among neighbor controllers are only needed at the event-triggered time. The proposed controller has been tested on a hardware-in-the-loop (HIL) platform, where the physical system is modeled in the OPAL-RT and the cyber system is realized in Raspberry Pis. The control effectiveness is validated by the HIL results.

In deregulated power systems, the distribution network operator (DNO) is not responsible for investment in distributed generation (DG) units, and they are just concerned about the best architecture ensuring a good service quality to their customers. The investment and operating decisions related to DG units are then taken by entities other than DNO which are exposed to uncertainty. The DNO should be able to evaluate the technical effects of these uncertain decisions. This paper proposes a fuzzy evaluation tool for analyzing the effect of investment and operation of DG units on active losses and the ability of distribution network in load supply at presence of uncertainties. The considered uncertainties are related to load values, installed capacity, and operating schedule of DG units. The proposed model is applied on a test system and also a real French urban network in order to demonstrate its functionality in evaluating the distribution expansion options.

The increasing integration of microgrids (MGs) in distribution networks forms the networked microgrids (NMGs). The peer-to-peer (P2P) control architecture is able to fully exploit the flexibility and resilience of NMGs. This paper proposes a multi-layer and multi-agent architecture to achieve P2P control of NMGs. The control framework is fully distributed and contains three control layers operated in the agent of each MG. For primary control, a droop control is adopted by each MG-agent for localized power sharing. For secondary control, a distributed consensus algorithm is proposed for frequency/voltage restoration and arbitrary power sharing among MGs. For tertiary control, the power loss in the NMG system is minimized by using alternating direction method of multipliers (ADMM). The architecture of the MG agent are designed, which processes the three control layers via information exchange among neighbouring agents. The proposed framework is validated in a hardware-in-the-loop test-bed, where NMG system with six buses and three MGs is emulated in the OPAL-RT, while the control algorithms are realized by a multi-agent system. The experimental results verify the effectiveness and advantages of the proposed framework, which demonstrates a practical control framework design for NMG systems.

This study proposes a probabilistic dynamic model for multi-objective distributed generation (DG) planning, which also considers network reinforcement at presence of uncertainties associated with the load values, generated power of wind turbines and electricity market price. Monte Carlo simulation is used to deal with the mentioned uncertainties. The planning process is considered as a two-objective problem. The first objective is the minimisation of total cost including investment and operating cost of DG units, the cost paid to purchase energy from main grid and the network reinforcement costs. The second objective is defined as the minimisation of technical risk, including the probability of violating the safe operating technical limits. The Pareto optimal set is found using non-dominated sorting genetic algorithm method and the final solution is selected using a max–min method. The model is applied on two distribution networks and compared with other models to demonstrate its effectiveness.

In deregulated power systems, distribution network operators (DNO) are responsible for maintaining the proper operation and efficiency of distribution networks. This is achieved traditionally through specific investments in network components. The event of distributed generation (DG) has introduced new challenges to these distribution networks. The role of DG units must be correctly assessed to optimise the overall operating and investment cost for the whole system. However, the distributed generation owners (DGOs) have different objective functions which might be contrary to the objectives of DNO. This study presents a long-term dynamic multi-objective model for planning of distribution networks regarding the benefits of DNO and DGOs. The proposed model simultaneously optimises two objectives, namely the benefits of DNO and DGO and determines the optimal schemes of sizing, placement and specially the dynamics (i.e. timing) of investments on DG units and network reinforcements over the planning period. It also considers the uncertainty of electric load, electricity price and wind turbine power generation using the point estimation method. The effect of benefit sharing is investigated for steering the decisions of DGOs. An efficient two-stage heuristic method is utilised to solve the formulated planning problem and tested on a real large-scale distribution network.