Optimal scheduling method of controllable loads in DC-smart house with deregulated electricity market
19 Dec 2013-pp 681-685
TL;DR: Simulation results indicate that a minimization of operational cost on both sides and a decrease of peak power demand are achieved by using the electricity market.
Abstract: The price of electricity has especially become expensive due to energy shortage with respect to the halting of nuclear power generation during peak hours in Japan. It is thought that a deregulated electricity market not only has a real chance to be an effective contribution to the reduction of price, but also a method for the efficient operation of thermal generators. From this background, this paper presents a method for optimal operation scheduling of controllable loads in the residential sector considering the environment of the electricity market with an aggregator who coordinates consumer demand for power receiving requests from the utility company. An optimization problem to determine the optimal price traded between the aggregator and consumers, and scheduling of controllable loads, are solved by proposed method. The simulation results indicate that a minimization of operational cost on both sides and a decrease of peak power demand are achieved by using the electricity market.
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TL;DR: In this paper, the optimal planning and operation of aggregated distributed energy resources (DER) with participation in the electricity market is analyzed, and the appropriate energy transactions for the aggregator in the wholesale day-ahead market according to the size of its aggregated resources.
69 citations
06 Jun 2016
TL;DR: In this article, the authors proposed a linear programming problem to find the optimal planning and operation of aggregated distributed energy resources (DER), managed by an aggregator that participates in the day-ahead wholesale electricity market as a price-maker agent.
Abstract: This paper proposes a linear programming problem to find the optimal planning and operation of aggregated distributed energy resources (DER), managed by an aggregator that participates in the day-ahead wholesale electricity market as a price-maker agent. The proposed model analyzes the impact of the size of the aggregated resources and gives the optimal planning and management of DER systems, and the corresponding energy transactions in the wholesale day-ahead market. The results suggest that when the aggregated resources are large enough, DER systems can achieve up to 32% extra economic benefits depending on the market share, compared with a business-as-usual approach (not implementing DER systems).
5 citations
24 Nov 2017
TL;DR: A general conceptual framework for the modelling of energy related activities in smart cities, based on determining the spheres of influence and intervention areas within the city, and on identifying agents and potential synergies among systems is presented.
Abstract: Models and simulators have been widely used in urban contexts for many decades. The drawback of most current models is that they are normally designed for specific objectives, so the elements considered are limited and they do not take into account the potential synergies between related systems. The necessity of a framework to model complex smart city systems with a comprehensive smart city model has been remarked by many authors. Therefore, this PhD thesis presents: i) a general conceptual framework for the modelling of energy related activities in smart cities, based on determining the spheres of influence and intervention areas within the city, and on identifying agents and potential synergies among systems, and ii) the development of a holistic energy model of a smart city for the assessment of different courses of action, given its geo-location, regulatory and technical constraints, and current energy markets. This involves the creation of an optimization model that permits the optimal planning and operation of energy resources within the city.
4 citations
Cites background from "Optimal scheduling method of contro..."
...For instance, in [194] an aggregator manages loads, distributed generation (DG) and EVs of a district with the particularity that the customers have elastic demand curves as linear functions of the electricity price, leading to an additional internal market between the aggregator and its prosumers....
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...[194] Microgrid, EV fleet Tabu search Internal market Shortterm Operation Price-maker...
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TL;DR: This paper presents a Mixed Integer Linear Programming (MILP) model to provide optimal solutions as well as a simple polynomial-time heuristic algorithm based on LP rounding, and demonstrates how to integrate the proposed scheduling approach in real-time charging operations.
Abstract: The Information and Communication Technologies (ICT) that are currently under development for future smart grid systems can enable load aggregators to have bidirectional communications with both the grid and Electric Vehicles (EVs) to obtain real-time price and load information, and to adjust EV charging schedules in real time. In addition, Energy Storage (ES) can be utilized by the aggregator to mitigate the impact of uncertainty and inaccurate prediction. In this paper, we study a problem of scheduling EV charging with ES from an electricity market perspective with joint consideration for the aggregator energy trading in the day-ahead and real-time markets. We present a Mixed Integer Linear Programming (MILP) model to provide optimal solutions as well as a simple polynomial-time heuristic algorithm based on LP rounding. In addition, we present a communication protocol for interactions among the aggregator, the ES, the power grid, and EVs, and demonstrate how to integrate the proposed scheduling approach in real-time charging operations. Extensive simulation results based on real electricity price and load data have been presented to justify the effectiveness of the proposed approach and to show how several key parameters affect its performance.
240 citations
TL;DR: In this paper, a genetic algorithm has been designed to optimize the parameters that define the best purchasing strategy of a retailer who supplies electricity to end-users in the short-term electricity market.
Abstract: This paper proposes a methodology for determining the optimal bidding strategy of a retailer who supplies electricity to end-users in the short-term electricity market. The aim is to minimize the cost of purchasing energy in the sequence of trading opportunities that provide the day-ahead and intraday markets. A genetic algorithm has been designed to optimize the parameters that define the best purchasing strategy. The proposed methodology has been tested using real data from the Spanish day-ahead and intraday markets over a period of two years with a significant cost reduction with respect to trading solely in the day-ahead market.
132 citations
TL;DR: In this article, a new method for determining the optimal bidding strategies among generating companies (GenCo) in the electricity markets using agent-based approach and numerical sensitivity analysis (NSA) is presented.
30 citations
TL;DR: In this paper, a new agent-based electricity market model is presented in which participants correspond to generation plants as well as storage power plants, and the profit is calculated based on an hourly price forward curve (HPFC), whereby the HPFC is constructed taking several factors into account.
Abstract: A new agent-based electricity market model is presented in which participants correspond to generation plants as well as storage power plants. In contrast to agent-based models where agents use learning heuristics and trial-and-error approaches to maximize their profits, the proposed model predictive bidding uses multi-step optimization to find bidding curves which maximize the expected discounted profit over a time horizon in the future. The profit is calculated based on an hourly price forward curve (HPFC), whereby the HPFC is constructed taking several factors into account. In addition, a price adjuster is used in these calculations which allows the participant to take into account his market power. The resulting optimization problem for each agent is solved using dynamic programming. A case study is carried out in which the proposed agent-based market model is applied to the four countries Switzerland, Germany, Italy, and France to study the effects of constrained cross-border capacities. The simulations show that the transmission system operators as well as the power generating units have no incentive to build additional cross-border capacity, since it lowers their profits.
25 citations