A simultaneous approach for optimal allocation of renewable energy sources and electric vehicle charging stations in smart grids based on improved GA-PSO algorithm

The significance and need of demand response (DR) programs is realized by the utility as a means to reduce the additional production cost imposed by the accelerating energy demand. With the development in smart information and communication systems, the price-based DR programs can be effectively utilized for controlling the loads of smart residential buildings. Nowadays, the use of stochastic renewable energy sources like photovoltaic (PV) by a small domestic consumer is increasing. In this paper, a generalized model for the residential load scheduling or load commitment problem (LCP) in the presence of renewable sources for any type of tariff is presented. Reinforcement learning (RL) is an efficient tool that has been used to solve the decision making problem under uncertainty. An RL-based approach to solve the LCP is also proposed. The novelty of this paper lies in the introduction of a comprehensive model with implementable solution considering consumer comfort, stochastic renewable power, and tariff. Simulation experiments are conducted to test the efficacy and scalability of the proposed algorithm. The performance of the algorithm is investigated by considering a domestic consumer with schedulable and nonschedulable appliances along with a PV source. Guidelines are given for choosing the parameters of the load.

Residential Load Scheduling With Renewable Generation in the Smart Grid: A Reinforcement Learning Approach

Investigating the impact of P2P trading on power losses in grid-connected networks with prosumers

Wind Power Distributed Generators (WPDGs) are being increasingly placed in the power system due to their several technical and environmental benefits. In this paper, a Modified Particle Swarm Optimizer (MPSO) based method is proposed for placement of multiple WPDGs and capacitors. Monte Carlo Simulation (MCS) based probabilistic load flow, considering uncertainty in load demand and wind generation, is developed. It is used to modify the WPDGs׳ and capacitors׳ sizes utilizing a sensitivity based approach, which maintains branch currents and bus voltages within their prescribed limits. The proposed method is simple, accurate and generic, and it can provide multiple choices to the utilities to place capacitors and WPDGs under various system constraints. Results on three distribution networks demonstrate the effectiveness of the proposed method. The impact of the DG placement on the system voltage profile, line loss, environment, and cost of generation has also been investigated on three distribution systems.

PSO based placement of multiple wind DGs and capacitors utilizing probabilistic load flow model

Distribution system operators are often challenged by voltage regulation problems, energy losses, and network capacity problems. This paper analyses a real-life 3.9-MVA distribution network in Gujarat State, India. Distributed generation from renewable energy sources like wind and solar, at optimal locations on distribution feeders, may enable energy loss reduction and voltage profile improvement. A methodology is developed and presented for deciding the appropriate location of these embedded renewable generators. Simulations are performed to calculate different scenarios, and the final analysis reveals that the low voltage problem has totally been eliminated on all of the nodes of the distribution network. Complimentary, significant energy loss reductions are also achieved in the distribution, and the network reserve capacity has also increased.

Dispersed Generation Enable Loss Reduction and Voltage Profile Improvement in Distribution Network—Case Study, Gujarat, India

The major issues always attached to conventional electrical Distribution Systems are poor voltage regulation so far the consumers are concerned and the high level of losses & limited or no reserve capacity of the Network so far as the utility is concerned. In this paper, an attempt has been made to address both issues by adding Dispersed Generation of appropriate capacity at appropriate location, using a methodology developed and described here. The study on a 2.8 MVA Radial Distribution Network having low voltage problem and high losses has been conducted and the results are found encouraging. For this purpose, the Network was simulated for obtaining existing status of voltage profile and losses and thereafter similar study was carried out by adding Renewable Distributed Generation. The analyses of results clearly show that there is no low voltage problem in any section of the Network, with a considerable reduction in losses. Incidentally, the reserve capacity of the Network is also increased giving a scope for future expansion.

Akash T. Davda

Papers

Dispersed Generation Enable Loss Reduction and Voltage Profile Improvement in Distribution Network—Case Study, Gujarat, India

Reduction of losses and improvement in voltage profile for a 2.8 MVA distribution network with Dispersed Generation