Distributed generation planning for diversified participants in demand response to promote renewable energy integration

Abstract

In modern distribution system, the distribution system operator (DSO) acts as a market facilitator and data manager as well as an energy supplier and operation controller. In this circumstance, the DSO should comprehensively consider the diversified participants of the modern distribution system when making investment decisions of distributed generation (DG). This paper proposes a DG planning model considering the behavior of the diversified participants, which are motivated to cooperate with distributed renewable energy resources to promote their integration, and to achieve the optimal DG investment plan. The optimization model takes a centralized structure but fully considers the preferences, profits and comfort levels of the aggregators and consumers. The model is linearized into a mixed-integer linear programming (MILP) problem and is solved by CPLEX. Results of the case study show that when the DSO spares subsidies to the aggregators and consumers to encourage their participation in demand response (DR) programs, it earns more compared with providing no subsidies for DR participation. It is also demonstrated that the overall profit increases as the subsidies increase within a certain range, but decreases when the subsidies exceed this range. Therefore, the DSO needs to carefully choose the subsidization level to achieve the optimal utilization of renewable energy and demand flexibility. The optimal subsidization level is derived from the model proposed in this paper. Therefore, this paper puts forward a new pattern to utilize the distributed renewable energy sources, and provides guidance in policy making and DR program implementation.

Optimal day‐ahead scheduling of multiple integrated energy systems considering integrated demand response, cooperative game and virtual energy storage

“…IES involves the production, transmission, and utilisation of multiple energy including electricity, heating, cooling, and gas [1,2], which can improve the overall energy utilisation efficiency by using and incorporating multiple energy sources to supply multiple energy demands [3,4]. Demand response (DR) is a significant way for the demand side to participate in grid interaction, which has been widely used for optimal planning [5][6][7][8] and optimal scheduling [9][10][11][12] in power systems to maintain frequency stability and power balance [13,14]. In general, the conventional electric DR (CEDR) This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.…”

Modelo conceptual para la concepción técnica de micro-redes: un caso de estudio en Colombia

“…Hoy día la red eléctrica tradicional se encuentra en un proceso de transformación a una red eléctrica moderna, eficiente y sostenible: la Smart Grid [1][2][3]. Para lograr esta transformación, la gestión de recursos energéticos distribuidos DER (Distributed Energy Resources) como la Generación Distribuida (GD), las Fuentes Convencionales y Fuentes No Convencionales de Energía Renovable (FCE y FNCER), el almacenamiento distribuido DS (Distributed Storage), la gestión de la demanda, la toma de mediciones en tiempo real, el control, entre otras, se constituyen en respuestas tecnológicas adecuadas, ya que ofrecen mejoras en aspectos técnicos, ambientales y sociales en lo concerniente a flexibilidad, escalabilidad energética, reducción en la emisión de gases contaminantes, electrificación de áreas remotas entre otras [4][5][6].…”

Smart Control of an Electric Vehicle for Ancillary Service in DC Microgrid

“…Generally, the main objective in an MG scheduling task is supplying power at minimum cost while ensuring maximum profit to the MG owners [8]. To this end, some studies in the literature have adopted the multi-objective optimisation approach by focusing on operational cost and emission minimisation [9]; energy cost minimisation and reliability consideration as energy loss index [10]; combined heat and power operation cost and energy trading from/to the grid as total MG operation cost [11]; DG units related costs, income and subsidy of Distribution System Operator (DSO) for electricity sales to aggregators as total cost of DSO [12]; and distributed energy resources (DERs) penetration and generation cost for microgrid energy supply [13]. Besides, benefit maximisation and cost minimisation have also been investigated to achieve optimal economic value [14].…”

“…[18] for its separated local and global iterative process which guarantees convergence accuracy. Mixed Integer Linear Programming is another popular technique -it involves the use of some linearisation approach to convert the nonlinear models into linear models [11], [12], [19]. Particle swarm optimisation, a heuristic approach, has also been proposed in the literature [19], [20].…”