Short-Term Electricity Price Forecasting Using a Combination of Neural Networks and Fuzzy Inference

This paper presents an artificial neural network, ANN, based approach for estimating short-term wholesale electricity prices using past price and demand data. The objective is to utilize the piecewise continuous na-ture of electricity prices on the time domain by clustering the input data into time ranges where the variation trends are maintained. Due to the imprecise nature of cluster boundaries a fuzzy inference technique is em-ployed to handle data that lies at the intersections. As a necessary step in forecasting prices the anticipated electricity demand at the target time is estimated first using a separate ANN. The Australian New-South Wales electricity market data was used to test the system. The developed system shows considerable im-provement in performance compared with approaches that regard price data as a single continuous time se-ries, achieving MAPE of less than 2% for hours with steady prices and 8% for the clusters covering time pe-riods with price spikes.

Multi-Deployment of Dispersed Power Sources Using RBF Neural Network

Multi-deployment of dispersed power sources became an important need with the rapid increase of the Distributed generation (DG) technology and smart grid applications. This paper proposes a computational tool to assess the optimal DG size and deployment for more than one unit, taking the minimum losses and voltage profile as objective functions. A technique called radial basis function (RBF) neural network has been utilized for such target. The method is only depending on the training process;so it is simple in terms of algorithm and structure and it has fast computational speed and high accuracy;therefore it is flexible and reliable to be tested in different target scenarios. The proposed method is designed to find the best solution of multi- DG sizing and deployment in 33-bus IEEE distribution system and create the suitable topology of the system in the presence of DG. Some important results for DG deployment and discussion are involved to show the effectiveness of our proposed method.

Regional Coordination for under Frequency Load Shedding

Frequency deviation can be used as an indicator of imbalance between supply and demand. When generation is insufficient, it can cause frequency decline in a power system operation. Implementing under frequency load shedding (UFLS) is one of the common methods to overcome this problem. This paper proposes a novel approach for adaptive load shedding. The concept is an extension of shared and targeted load shedding using reserve margin. The optimal system configuration is then selected from those candidates to fulfill operational objectives. Operational constraints related to system parameters, threshold frequency, total of load shed and control area including line capacity are considered. An example using four sub-areas connected to an external system shows that the proposed regional coordination as an adaptive UFLS is feasible.

Utilization of Energy Capacitor Systems in Power Distribution Networks with Renewable Energy Sources

The impact of power fluctuation caused by renewable sources is highly negative. This article discusses the idea of an energy capacitor system (ECS) which regulates the power balance in a distribution system based on Multi-Agent System (MAS). Energy Capacitor system as a storage device plays the main role to control the system’s power quality by absorbing the fluctuations. Load Following Operation (LFO) process and coordination control scheme between the ECS and diesel generator have been introduced. Results show the efficient utilization of the ECS based on a special index defined in this paper to evaluate the power fluctuations in the distribution system. The results also show the useful implementation of the control scheme by revealing the capability of keeping the ECS stored energy in the specified range.