The concept of Transactive energy (TE) been adapted in the regulation of electricity market within the context of economic planning and control for grid reliability enhancement. The objective is to improve productivity and participation of the players in the market that is composed of distributed energy resources (DER). The main goal of implementing a market structure based on TE is to secure permission for the market players so that they could attain a higher payoff. In this study, an optimization-based algorithm in which an objective function premised on economic strategies, distribution limitations and the overall demand in the market structure is proposed. The objective function is solved for near global optima using four heuristically guided optimization algorithms. The proposed algorithm which ensures that
Peak shaving, demand response, fast fault detection, emissions and costs reduction are some of the main objectives to meet in advanced district heating and cooling (DHC) systems. In order to enhance the operation of infrastructures, challenges such as supply temperature reduction and load uncertainty with the development of algorithms and technologies are growing. Therefore, traditional control strategies and diagnosis approaches cannot achieve these goals. Accordingly, to address these shortcomings, researchers have developed plenty of innovative methods based on their applications and features. The main purpose of this paper is to review recent publications that include both hard and soft computing implementations such as model predictive control and machine learning algorithms with applications also on both fourth and fifth generation district heating and cooling networks. After introducing traditional approaches, the innovative techniques, accomplished results and overview of the main strengths and weaknesses have been discussed together with a description of the main capabilities of some commercial platforms.
The desire to increase energy efficiency and reliability of power grids, along with the need for reducing carbon emissions has led to increasing the utilization of Home Micro-grids (H-MGs). In this context, the issue of economic emission dispatch is worthy of consideration, with a view to controlling generation costs and reducing environmental pollution. This paper presents a multi-objective energy management system, with a structure based on demand response (DR) and dynamic pricing (DP). The proposed energy management system (EMS), in addition to decreasing the market clearing price (MCP) and increasing producer profits, has focused on reducing the level of generation units emissions, as well as enhancing utilization of renewable energy units through the DR programs. As a consequence of the nonlinear and discrete nature of the H-MGs, metaheuristic algorithms are applied to find the best possible solution. Moreover, due to the presence of generation units, the Taguchi orthogonal array testing (TOAT) method has been utilized to investigate the uncertainty regarding generation units. In the problem being considered, each H-MG interacts with each other and can negotiate based on their own strategies (reduction of cost or pollution). The obtained results indicate the efficiency of the proposed algorithm, a decrease in emissions and an increase in the profit achieved by each H-MG, by 37% and 10%, respectively.
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