Demand side management studies on distributed energy resources: A survey

The number of distributed environmentally friendly energy sources and generators necessitates new operating methods and a power network board to preserve or even increase the efficiency and quality of the power supply. Similarly, the growth of matriculates promotes the formation of new institutional systems, in which power and power exchanges become increasingly essential. Because of how an inactive entity traditionally organizes distribution systems, the DG’s connection inevitably changes the system’s qualifications to which it is connected. As a consequence of the Distributed Generation, this presumption is currently legal and non-existent. This article glides on demand side management and analysis on distributed energy resources. Investigation of DSM along with zonal wise classification has been carried out in this survey. Its merits and applications are also presented.Universidad Tecnológica de Bolíva

Demand Side Management Studies on Distributed Energy Resources: A Survey

The number of distributed environmentally friendly energy sources and generators necessitates new operating methods and a power network board to preserve or even increase the efficiency and quality of the power supply. Similarly, the growth of matriculates promotes the formation of new institutional systems, in which power and power exchanges become increasingly essential. Because of how an inactive entity traditionally organizes distribution systems, the DG’s connection inevitably changes the system’s qualifications to which it is connected. As a consequence of the Distributed Generation, this presumption is currently legal and non-existent. This article glides on demand side management and analysis on distributed energy resources. Investigation of DSM along with zonal wise classification has been carried out in this survey. Its merits and applications are also presented

Optimized energy consumption model for smart home using improved differential evolution algorithm

Abstract: This paper proposes an improved enhanced differential evolution algorithm for implementing demand response between aggregator and consumer. The proposed algorithm utilizes a secondary population archive, which contains unfit solutions that are discarded by the primary archive of the earlier proposed enhanced differential evolution algorithm. The secondary archive initializes, mutates and recombines candidates in order to improve their fitness and then passes them back to the primary archive for possible selection. The capability of this proposed algorithm is confirmed by comparing its performance with three other wellperforming evolutionary algorithms: enhanced differential evolution, multiobjective evolutionary algorithm based on dominance and decomposition, and non-dominated sorting genetic algorithm III. This is achieved by testing the algorithms’ ability to optimize a multiobjective optimization problem representing a smart home with demand response aggregator. Shiftable and non-shiftable loads are considered for the smart home which model energy usage profile for a typical household in Johannesburg, South Africa. In this study, renewable sources include battery bank and rooftop photovoltaic panels. Simulation results show that the proposed algorithm is able to optimize energy usage by balancing load scheduling and contribution of renewable sources, while maximizing user comfort and minimizing peak-to-average ratio

APPLICATION OF LOAD SHIFTING FOR COMMERCIAL HVAC-R SYSTEMS VIA STATIC AND DYNAMIC EVENT AUTOMATED DEMAND RESPONSE

Life in the modern era is inextricably tied to energy and the unending, ever-growing need for more. The technologies that drive society, in fields such as communication, infrastructure, and heavy industry, are dependent on that electrical energy being reliable and readily available. This places an extreme importance on the power generation sector as a function of meeting the demand required for stability. However, as seen with increased climate volatility due to misused or otherwise mismanaged resources in the heavy industry, and the uncertain and variable renewable energy generation; there must also be ecological as well as economic consideration when discussing modern energy. Therefore, a balance between sustainability and energy demand must be met, to increase the desire for better use of energy resources. This thesis focuses on an application of a demand response (DR) program that may increase the performance of energy resources while also maximizing monetary savings for end-users in the commercial sector. This DR may provide support for the uncertain and intermittent nature of renewable generation. By targeting Heating, Ventilation, Air Conditioning, and Refrigeration loads (HVAC-R) utilities have access to non-critical loads that they may be able to shed during times of high energy demand. This allows for the allocation of less resources during peak hours, which may lead to less strain the electrical grid and could increase the threshold for which additional energy resources will need to be constructed. Based on relay to substitute the analog parts of a refrigerator and a development PC to drive the control logic a closed loop, Automated Demand Response (ADR) is implemented to utilize both static and dynamic Time of Use (TOU) events as well as Critical Peak Pricing (CPP) events. This enables the end user to respond to two types of unique events, resulting in potential increased energy efficiency, and monetary savings for the end user via load shifting

Microload Management in Generation Constrained Power Systems

The reasons for power systems' outages can be complicated and difficult to pinpoint, but an obvious shortfall in generation compared to electricity demand has been identified as the major cause of load shedding in generation constrained power systems. A sudden rise in demand for electricity on these networks at any time could result in a total collapse of the entire grid. Therefore, in this thesis, algorithms to efficiently allocate the available generation are investigated to prevent the associated hardships and lose experience by the final consumers and the electric utility suppliers, respectively. Heuristic technique is utilised by developing various dynamic programming-based algorithms to achieve the constraints of uniquely controlling home appliances to reduce the overall demands for electricity by the consumers within the grid in context. These algorithms are focused on the consumers' comfort and the associated benefits to the electricity utility company in the long run. The evaluation of the proposed approach is achieved through microload management by employing three main techniques; General Shedding (GS), Priority Based Shedding (PBS) and Excess Reuse Shedding (ERS). These techniques were evaluated using both Grouped and “UnGrouped” microloads based on how efficient the microload managed the available generation to prevent total blackouts. A progressive reduction in excess microload shedding experienced by GS, PBS, and the ERS shows the proposed algorithms' effectiveness. Further, predictive algorithms are investigated for microload forecasting towards microload management to prepare both consumers and the electric utility companies for any impending load shedding. Measuring the forecasting accuracy and the root mean square errors of the models evaluated proved the potential for microload demand prediction