10This paper proposes a planning model for power distribution companies (DISCOs) 11 to maximize profit. The model determines optimal network location and capacity for 12 renewable energy source, which are categorized as independent power production (IPP) 13 and self-generation (SG). IPP refers to generators owned by third-party investors and 14 linked to a quota obligation mechanism. SG encompasses smaller generators, supported 15 by feed-in tariffs, that produce energy for local consumption, exporting any surplus 16 generation to the distribution network. The obtained optimal planning model is able 17 to evaluate network capacity to maximize profit when the DISCO is obliged to provide 18 network access to SG and IPP. Distinct parts of the objective function, owing to the 19 definition of SG, are revenue erosion, recovery as well as the cost of excess energy. 20Together with the quota mechanism for IPP, the combination of all profit components 21 creates a connection trade-off between IPP and SG for networks with limited capacity. 22The effectiveness of the model is tested on 33-and 69-bus test distribution systems 23 and compared to standard models that maximize generation capacity with predefined 24 capacity diffusion. Simulation results demonstrate the model outperforms the standard 25 models in satisfying the following binding constraints: minimum IPP capacity and SG 26 net energy. It is further revealed that integrating SG and IPP with the proposed model 27 increases profit by up to 23.7%, adding an improvement of 8% over a feasible standard

An emerging theme in the development of supporting facilities for plug-in electric vehicles (EVs) is the costeffective planning and utilisation of charging networks consistent with the uptake of EVs. This paper proposes a low voltage direct current (LVDC) reconfigurable charging network for plugin electric vehicles (EVs) and presents a functional energy management system (EMS) that is capable of planning and operating the charging network to minimise charging cost and to facilitate progressive infrastructure deployment based on EV demand. The charging network is connected to the main AC grid through one or more centralised AC/DC converters that supply a high power charge to EVs connected to the DC side of the converters. The EMS accommodates multiple parking bays, charging sources, AC constraints, non-linear EV battery loads and user charging requirements with a novel approach to managing user inconvenience. The inconvenience model is founded on the presence of user flexibility i.e., an allowance on charging time or battery SOC, providing the capability to increase asset utilisation and enable access for additional network users. Through a series of case studies and a stochastic forecasting approach, the reconfigurable network and EMS demonstrate the capacity to achieve savings over fixed AC and sequential DC systems. Index Terms-Plug-in electric vehicles, direct current charging systems, mixed-integer program, charging infrastructure planning, multiple-charger control.

This paper investigates daily volt/var control in distribution networks using feeder capacitors as well as substation capacitors paired with on-load tap changers. A twostage coordinated approach is proposed. Firstly, the feeder capacitor dispatch schedule is determined based on reactive power heuristics. Then, an optimisation model is applied to determine the dispatch schedule of the substation devices taking into account the control actions of the feeder capacitors. The reference voltage of the substation secondary bus and the tap position limits of transformers are modified such that the model adapts to varying load conditions. The optimisation model is solved with a modified particle swarm optimisation algorithm. Furthermore, the proposed method is compared with conventional volt/var control strategies using a distribution network case study. It is demonstrated that the proposed approach performs better than the conventional strategies in terms of voltage deviation and energy loss minimisation.Dispatch of the volt/var control (VVC) resources can be performed in a coordinated manner in constrained environments to meet specific operational objectives [2, 3, 4, 5 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]. The complexity of the objective function, constraints, and computation is influenced by, among others: regulatory limits, switching limitations and available control devices. The focus of this paper is daily VVC with switching restrictions, which is usually applied to networks with widespread communication and control coverage [7,8,9,10, 11,12,13,14,15,16,17]. The ad-10 ditional requirement of this VVC approach is a day-ahead forecast of load behaviour, which is made possible by the existence of load forecasting techniques that provide good accuracy [18], [19].Daily coordinated control of all distribution devices is computationally complex, but there are a number of ways to deal with this difficulty. One way to approach this is to 15 simplify the solution space so as to reduce the computational burden. For instance, the requirements of dynamic programming can be eased according to [8], [9], [11], while a more efficient solver based on the interior-point method is presented in [17]. In [10] the number of possible states is decreased by combining artificial neural networks, a rulebased method and dynamic programming. Use of heuristic rules can also reduce the 20 number of possible device operations, therefore simplifying the optimisation model [13].These methods address the issue of computational complexity but the requirements for remote control infrastructure remain for network-wide implementation. Another alternative is to divide the scheduling problem into two sub-problems: one handling the dispatch of the substation capacitor (SC) and OLTC, and the other controlling the 25 feeder capacitors (FCs) [12]. In particular, dispatch of the substation devices minimises reactive power-flow and the voltage deviation at the substation bus. FCs are then dispatched based on local bus voltage and power factor devi...