Performance Evaluation of Different Optimization Algorithms for Power Demand Forecasting Applications in a Smart Grid Environment

AbstractThis paper presents an in-depth performance evaluation of three different optimization algorithms, in particular genetic algorithm (GA), particle swarm optimization (PSO), and firefly (FF) algorithm for power demand forecasting in a deregulated electricity market and smart grid environments. In this framework, this paper proposes a hybrid intelligent algorithm for power demand forecasts using the combination of wavelet transform (WT) and fuzzy ARTMAP (FA) network that is optimized by using FF optimization algorithm. The effectiveness and accuracy of the proposed hybrid WT+FF+FA model is trained and tested utilizing the data obtained from ISO-NE electricity market

Forecasting Power Output of Solar Photovoltaic System Using Wavelet Transform and Artificial Intelligence Techniques

AbstractWith increased penetration of solar as a variable energy resource (VER), solar photovoltaic (PV) power production is rapidly increasing into large-scale power industries. Since power output of PV systems depends critically on the weather, unexpected variations of their power output may increase the operating costs of the power system. Moreover, a major barrier in integrating this VER into the grid is its unpredictability, since steady output cannot be guaranteed at any particular time. This biases power utilities against using PV power since the planning and overall balancing of the grid becomes very challenging. Developing a reliable algorithm that can minimize the errors associated with forecasting the near future PV power generation is extremely beneficial for efficiently integrating VER into the grid. PV power forecasting can play a key role in tackling these challenges. This paper presents one-hour-ahead power output forecasting of a PV system using a combination of wavelet transform (WT) and artificial intelligence (AI) techniques by incorporating the interactions of PV system with solar radiation and temperature data. In the proposed method, the WT is applied to have a significant impact on ill-behaved PV power time-series data, and AI techniques capture the nonlinear PV fluctuation in a better way

Harmonic Distortion Minimization in Power Grids with Wind and Electric Vehicles

Power-electronic interfacing based devices such as wind generators (WGs) and electrical vehicles (EVs) cause harmonic distortions on the power grid. Higher penetration and uncoordinated operation of WGs and EVs can lead to voltage and current harmonic distortions, which may exceed IEEE limits. It is interesting to note that WGs and EVs have some common harmonic profiles. Therefore, when EVs are connected to the grid, the harmonic pollution EVs impart onto the grid can be reduced to some extent by the amount of wind power injecting into the grid and vice versa. In this context, this work studies the impact of EVs on harmonic distortions and careful utilization of wind power to minimize the distortions in distribution feeders. For this, a harmonic unbalanced distribution feeder model is developed in OpenDSS and interfaced with Genetic Algorithm (GA) based optimization algorithm in MATLAB to solve optimal harmonic power flow (OHPF) problems. The developed OHPF model is first used to study impact of EV penetration on current/voltage total harmonic distortions (THDs) in distribution grids. Next, dispatch of WGs are found at different locations on the distribution grid to demonstrate reduction in the current/voltage THDs when EVs are charging

Optimal Operation Method for Distribution Systems Considering Distributed Generators Imparted with Reactive Power Incentive

In order to solve urgent energy and environmental problems, it is essential to carry out high installation of distributed generation using renewable energy sources (RESs) and environmentally-friendly storage technologies. However, a high penetration of RESs usually leads to a conventional power system unreliability, instability and low power quality. Therefore, this paper proposes a reactive power control method based on the demand response (DR) program to achieve a safe, reliable and stable power system. This program does not enforce a change in the active power usage of the customer, but provides a reactive power incentive to customers who participate in the cooperative control of the distribution company (DisCo). Customers can achieve a reduction in their total energy purchase by gaining a reactive power incentive, whilst the DisCo can achieve a reduction of its total procurement of equipment and distribution losses. An optimal control schedule is calculated using the particle swarm optimization (PSO) method, and also in order to avoid over-control, a modified scheduling method that is a dual scheduling method has been adopted in this paper. The effectiveness of the proposed method was verified by numerical simulation. Then, simulation results have been analyzed by case studies

Optimizing Re-planning Operation for Smart House Applying Solar Radiation Forecasting

This paper proposes the re-planning operation method using Tabu Search for direct current (DC) smart house with photovoltaic (PV), solar collector (SC), battery and heat pump system. The proposed method is based on solar radiation forecasting using reported weather data, Fuzzy theory and Recurrent Neural Network. Additionally, the re-planning operation method is proposed with consideration of solar radiation forecast error, battery and inverter losses. In this paper, it is assumed that the installation location for DC smart house is Okinawa, which is located in Southwest Japan. The validity of proposed method is confirmed by comparing the simulation results

Optimum Capacity and Placement of Storage Batteries Considering Photovoltaics

In recent years, due to the enforcement of the Feed-in tariff (FIT) scheme for renewable energy, a large number of photovoltaic (PV) has been introduced, which causes fluctuations in the supply-demand balance of a power system. As measures against this, the introduction of large capacity storage batteries and demand response has been carried out, and the balance between supply and demand has been adjusted. However, since the increase in capacity of the storage battery is expensive, it is necessary to optimize the capacity of the storage battery from an economic point of view. Therefore, in the power system to which a large amount of photovoltaic power generation has been introduced, the optimal capacity and optimal arrangement of storage batteries are examined. In this paper, the determination of storage battery placement and capacity considering one year is performed by three-step simulation based on probability density function. Simulations show the effectiveness of storage batteries by considering the introduction of demand response and comparing with multiple cases

Efficient transactive control for energy storage management system in prosumer-centric networked microgrids

This paper presents a transactive control (TC) mechanism for the management of battery energy storage systems (BESS) in residential networked microgrids (MGs) that contain loads, electric vehicles (EVs), and rooftop solar photovoltaic systems (PV). The goals of the TC are to maximize the savings of consumers and prosumers and to reduce peak load on local transformers. This is accomplished by utilizing local hybrid PV-BESS resources from prosumer community groups (PCGs), which are scheduled to offset peak loads. A model predictive control (MPC) based method is utilized to optimize the BESS scheduling. In the proposed TC, the PCGs are incentivized by the distribution system operator (DSO) through a dynamic price signal that is being updated hourly based on the MG local conditions. To evaluate the proposed TC, case studies are conducted on residential MGs located in an IEEE 33-bus test system. The evaluation indicates that the proposed TC can improve the savings of prosumers/consumers, reduce peak demand caused by EV charging in the distribution networks, and is able to alleviate undesired grid effects, e.g., transformer overloads