Integrated demand response in district electricity-heating network considering double auction retail energy market based on demand-side energy stations

Wang, Dan; Hu, Qing’e; Jia, Hongjie; Hou, Kai; Du, Wei; Chen, Ning; Wang, Xudong; Fan, Menghua

doi:10.1016/j.apenergy.2019.04.050

Cited by 78 publications

(26 citation statements)

References 31 publications

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“…The factors that affect the total load demand in the area are mainly the development law of load itself and social and economic development [15,19]. For the multi-energy coupling self-sufficient power, the main influencing factors are the direct offset effect of regional energy production, the energy economic market, etc.…”

Section: Key Influencing Factorsmentioning

confidence: 99%

“…At the same time, they also realize the absorption of part of renewable energy [13]; according to the multi-energy complementary system proposed by Xuebin Wang et al, which mainly focuses on renewable energy, they reduce the volatility of thermal power generation and improve the absorption capacity of new energy through adaptive adjustment of hydropower [14]. In the demand response system proposed by Dan Wang et al, they achieved 80%-90% renewable energy absorption and 18.76% energy expenditure savings through accurate control of electric heating, interactive competition strategy of supply and demand in the power retail market, and economic means [15]. There is no doubt that these research results have made important contributions to the efficiency improvement and structural optimization of integrated energy utilization.…”

Section: Introductionmentioning

confidence: 99%

“…Coal and natural gas work by converting chemical energy into heat. In this conversion process, there is energy loss caused by energy conversion efficiency, and energy loss taken away by residue and waste gas after conversion [15]. Without multi-energy coupling design, its energy flow is shown in Figure 3a.…”

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confidence: 99%

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Power Load Demand Forecasting Model and Method Based on Multi-Energy Coupling

et al. 2020

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At the present stage, China’s energy development has the following characteristics: continuous development of new energy technology, continuous expansion of comprehensive energy system scale, and wide application of multi-energy coupling technology. Under the new situation, the accurate prediction of power load is the key to alleviate the problem that the planning and dispatching of the current power system is more complex and more demanding than the traditional power system. Therefore, firstly, this paper designs the calculation method of the power load demand of the grid under the multi-energy coupling mode, aiming at the important role of the grid in the power dispatching in the comprehensive energy system. This load calculation method for regional power grid operating load forecasting is proposed for the first time, which takes the total regional load demand and multi-energy coupling into consideration. Then, according to the participants and typical models in the multi-energy coupling mode, the key factors affecting the load in the multi-energy coupling mode are analyzed. At this stage, we fully consider the supply side resources and the demand side resources, innovatively extract the energy system structure characteristics under the condition of multi-energy coupling technology, and design a key factor index system for this mode. Finally, a least squares support vector machine optimized by the minimal redundancy maximal relevance model and the adaptive fireworks algorithm (mRMR-AFWA-LSSVM) is proposed, to carry out load forecasting for multi-energy coupling scenarios. Aiming at the complexity energy system analysis and prediction accuracy improvement of multi-energy coupling scenarios, this method applies minimal redundancy maximal relevance model to the selection of key factors in scenario analysis. It is also the first time that adaptive fireworks algorithm is applied to the optimization of adaptive fireworks algorithm, and the results show that the model optimization effect is good. In the case of A region quarterly load forecasting in southwest China, the average absolute percentage error of a least squares support vector machine optimized by the minimal redundancy maximal relevance model and the adaptive fireworks algorithm (mRMR-AFWA-LSSVM) is 2.08%, which means that this model has a high forecasting accuracy.

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Section: Key Influencing Factorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Power Load Demand Forecasting Model and Method Based on Multi-Energy Coupling

et al. 2020

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“…The results show that the method can relieve congestion in weak lines. Dan Wang et al [17] constructed the electricity-heat coordinated retail market framework to achieve the coordinated settlement of electric and heating load, managing the distributed energy generation and consumption through coordinated control method. The results show that the method can optimize the resource allocation of demand-side energy stations and the energy use of customers by economic means.…”

Section: State Of the Artmentioning

confidence: 99%

Study on Pricing Mechanism of Cooling, Heating, and Electricity Considering Demand Response in the Stage of Park Integrated Energy System Planning

Liu

2020

Applied Sciences

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With the opening of the Chinese electricity market, as a retailer that provides energy services to consumers, the park-integrated energy system (PIES) not only serves as an effective way to earn benefits and reduce carbon emissions but also impacts the energy consumption characteristics of consumers. The PIES implements this function by adjusting the energy selling price in free energy markets. The pricing mechanism model (P-M model) is established to obtain the energy selling price in the planning and design stages. In this model, the impact of the demand response on the energy configuration and the impact of the changes in energy configuration on the energy cost price are both considered. Additionally, the optimal result ensures that both the consumers and the PIES benefit simultaneously. The reactive demand response zone, which represents a consumer trap, is found in numerical studies. The results indicate the following: (1) from the perspective of P-M model optimization, the benefit exclusive point of the PIES is the optimal solution in the short term; (2) from the perspective of the long-term benefit, the ultimate result in the relationship between the PIES and consumers is that the PIES will share its profits with consumers; in other words, benefit sharing point is the optimal solution for the long term.

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“…(2) Consider the influence of energy trade market on the planning link Urban energy trade can improve economic efficiency and the reliability of integrated energy system operation. Besides, it can enhance environmental interests and promote employment, [12]. But conflicts may appear between them.…”

Section: Expansion Of Planning Boundariesmentioning

confidence: 99%

Application Challenges and Prospects of Urban Integrated Energy System Planning

Zhihong

Zhu

et al. 2020

E3S Web Conf.

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The integrated energy system is the main physical carrier of the energy Internet. Planning is the leading link of its application, which directly affects the necessity and economy of the construction of the project. Firstly, the basic process of integrated energy planning is summarized. Then, three application challenges faced by urban integrated energy system planning are summarized from the perspective of engineering application: integrated energy system refinement modelling, multi-energy load integrated prediction, and expansion of planning boundaries. The corresponding strategies are given for the above challenges, and the development of integrated energy system planning and application is prospected, which provides reference for the staff engaged in urban integrated energy system planning and related investors.

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Integrated demand response in district electricity-heating network considering double auction retail energy market based on demand-side energy stations

Cited by 78 publications

References 31 publications

Power Load Demand Forecasting Model and Method Based on Multi-Energy Coupling

Power Load Demand Forecasting Model and Method Based on Multi-Energy Coupling

Study on Pricing Mechanism of Cooling, Heating, and Electricity Considering Demand Response in the Stage of Park Integrated Energy System Planning

Application Challenges and Prospects of Urban Integrated Energy System Planning

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