Feasible electricity price calculation and environmental benefits analysis of the regional nighttime wind power utilization in electric heating in Beijing

Ren

et al. 2020

Processes

Aimed at the coordination control problem of each unit caused by microgrid participation in the spot market and considering the randomness of wind and solar output and the uncertainty of spot market prices, a day-ahead real-time two-stage optimal scheduling model for microgrid was established by using the chance-constrained programming theory. On this basis, an improved particle swarm optimization (PSO) algorithm based on stochastic simulation technology was used to solve the problem and the effect of demand side management and confidence level on scheduling results is discussed. The example results verified the correctness and effectiveness of the proposed model, which can provide a theoretical basis in terms of reasonably coordinating the output of each unit in the microgrid in the spot market.

Section: The Impact Analysis Of Demand Side Managementmentioning

confidence: 99%

A Two-Stage Optimal Scheduling Model of Microgrid Based on Chance-Constrained Programming in Spot Markets

Ren

et al. 2020

Processes

“…Heat load in the heating system is supplied by the CHP system, the heat pump (HP) Power load in the power system is supplied by the distributed or cluster renewable energy system (RES), the combined heat and power (CHP) system, the energy storage (ES) system and the upper grid. Heat load in the heating system is supplied by the CHP system, the heat pump (HP) system and the heat storage system [22]. Cooling load in the cooling system is transformed and supplied by the absorption refrigeration (AR) system, the refrigerating machine (RM) system and the cooling storage system.…”

Section: Typical Structure Of the Electric-gas Interconnection Systemmentioning

confidence: 99%

Robust Optimization Model for Energy Purchase and Sale of Electric–Gas Interconnection System in Multi-Energy Market

Yang

et al. 2019

Applied Sciences

Self Cite

With the increasing coupling of the power system and the natural gas system, the electric-gas interconnection system has become a typical form of comprehensive energy utilization. Through the energy conversion function of the coupling unit, the system can flexibly participate in the bidding for purchasing and selling energy in a power market and a natural gas market on the premise of meeting the internal demand of multiple loads. To solve the internal coordination and optimization problem and the external flexible bidding problem in the multi-energy market, this paper proposes a robust optimization model of energy purchase and sale for the electric-gas interconnection system in a multi-energy market. Firstly, the basic structure of the electric-gas interconnection system is introduced, and the steady-state model of energy flow in the system is built based on the energy hub model. Secondly, considering the uncertainty of energy prices and the output power of renewable energy units in the system, a bidding model for energy purchase and sale of the electric-gas interconnection system in multi-energy market based on the idea of robust optimization is constructed in the framework of the Nordic energy market. Finally, empirical analysis based on the actual data is carried out, and the results prove the validity and superiority of the model. In this paper, aiming at the uncertainty of energy price, a large number of scenes are generated by Latin hypercube sampling (LHS), and then a k-means algorithm is used to reduce the scenes, so as to simulate typical scenes. Aiming at the uncertainty of the output power of the renewable energy unit in the system, a cardinal uncertainty set is used to control deviation between the actual output power and predicted output power, so that the overall robustness of the model can be controlled. The proposed model can make decision-making independent of the accurate probability distribution of uncertainty factors, and is suitable for complex multi energy systems. Meanwhile, the model possesses excellent robustness, which can effectively reduce the risk of bidding loss in the process of energy purchase and sale. Appl. Sci. 2019, 9, 5497 2 of 21 structure, price mechanism and price volatility of heterogeneous energy sources, the decision-makers' bidding decisions should not only consider the internal influences of output power characteristics and the coupling situation of multi-type heterogeneous energy sources, but also consider the external influence of the multi-dimensional market structure and the multi-level price system, which will bring enormous challenges for bidding decision-makers [6]. Therefore, it is urgent to carry out research on the bidding strategy of the integrated energy system in a multi-energy market, which will provide a scientific and reasonable decision-making basis for both the internal multi-energy collaborative operation management and the external multi energy market bidding.The electric-gas interconnection system, mostly based on industrial parks, is an integra...

“…The demand in the wholesale market is 800 MW, and the demand in the distribution market is 300 MW. The parameter settings of power producers, wind storage joint ventures, and middlemen are shown in Table 1 [22,23]. For convenience of calculation, assume that the upper limit of the wind power supplier's output is 200 MW.…”

Section: Basic Datamentioning

confidence: 99%

Game Analysis of Wind Storage Joint Ventures Participation in Power Market Based on a Double-Layer Stochastic Optimization Model

Geng

et al. 2019

Processes

The volatility of a new energy output leads to bidding bias when participating in the power market competition. A pumped storage power station is an ideal method of stabilizing new energy volatility. Therefore, wind power suppliers and pumped storage power stations first form wind storage joint ventures to participate in power market competition. At the same time, middlemen are introduced, constructing an upper-level game model (considering power producers and wind storage joint ventures) that forms equilibrium results of bidding competition in the wholesale and power distribution markets. Based on the equilibrium result of the upper-level model, a lower model is constructed to distribute the profits from wind storage joint ventures. The profits of each wind storage joint venture, wind power supplier, and pumped storage power station are obtained by the Nash negotiation and the Shapely value method. Finally, a case study is conducted. The results show that the wind storage joint ventures can improve the economics of the system. Further, the middlemen can smooth the rapid fluctuation of power price in the distribution and wholesale market, maintaining a smooth and efficient operation of the electricity market. These findings provide information for the design of an electricity market competition mechanism and the promotion of new energy power generation.