A new approach to ensure successful implementation of sustainable demand side management (DSM) in South African mines

Demand side management (DSM) is seen as a short-term solution to the imminent problem of electricity supply shortages in South Africa. DSM aims to reduce peak loads with immediate results in a short time. The mining sector in South Africa is a large energy user with pumping one of the largest consuming systems. Therefore, DSM potential (load shift) should be investigated on these pumping systems.     

Optimal real time pricing in an agent-based retail market using a comprehensive demand response model

In this paper, a weighted combination of different demand vs. price functions referred to as Composite Demand Function (CDF) is introduced in order to represent the demand model of consuming sectors which comprise different clusters of customers with divergent load profiles and energy use habitudes. Derived from the mathematical representations of demand, dynamic price elasticities are proposed to demonstrate the customers’ demand sensitivity with respect to the hourly price. Based on the proposed CDF and dynamic elasticities, a comprehensive demand response (CDR) model is developed in this paper for the purpose of representing customer response to time-based and incentive-based demand response (DR) programs. The above model helps a Retail Energy Provider (REP) agent in an agent-based retail environment to offer day-ahead real time prices to its customers. The most beneficial real time prices are determined through an economically optimized manner represented by REP agent’s learning capability based on the principles of Q-learning method incorporating different aspects of the problem such as price caps and customer response to real time pricing as a time-based demand response program represented by the CDR model. Numerical studies are conducted based on New England day-ahead market’s data to investigate the performance of the proposed model.     

Optimal bidding strategy in a competitive electricity market based on agent-based approach and numerical sensitivity analysis

The objective of this study is to present a new method for determination of the optimal bidding strategies among generating companies (GenCo) in the electricity markets using agent-based approach and numerical sensitivity analysis (NSA). While agent-based approach provides for decision making, NSA can help with identifying the critical control points that lead to proper decisions to be taken by GenCos. To achieve the objective, the pricing mechanism used for settling the electricity market and determining the GenCos rewards is locational marginal pricing (LMP) and the sensitivity of each GenCo reward with respect to its bid is analyzed, then, the optimal strategy is determined. An example and a case study are used to illustrate the efficiency of the proposed method. The LMPs and allocated generations of GenCos show that the proposed method leads GenCos to learn a strategic manner and, as a result, increase prices and maximize their rewards. To validate the proposed method, the results from this study are compared with those available in the literature. The comparison of results shows an improved simulation time by 8.16 percent and total reward of market by 2.46 percent.