A dynamic model to optimize municipal electric power systems by considering carbon emission trading under uncertainty

Zhu, Ying; Li, Y. P.; Huang, Guohe; Fan, Yurui; Nie, S.

doi:10.1016/j.energy.2015.05.106

Cited by 28 publications

(8 citation statements)

References 34 publications

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“…The actual carbon emission of thermal units is related to the power load rate. Generally speaking, the actual carbon emission of units can be expressed as a quadratic function, similar to (22):…”

Section: Mathematical Model With Cetmentioning

confidence: 99%

“…Valley load period Float load period Peak load period Period 0:00-6:00; 22:00-24:00 6:00-9:00; 14:00-19:00 9:00-14:00; 19:00-22:00 the grid purchase price of thermal power is 380 ¥/MW⋅h, and the price of standard coal is 800 ¥/t. The wind power unit equivalent utilization rate and system load distribution are set according to the literature [22] and listed in Table 4.…”

Section: Loadmentioning

confidence: 99%

“…CET can highlight the environmental friendliness of wind power and increase the advantages of wind power generation [21]. Zhu et al [22] developed a full-infinite fuzzy stochastic programming method for planning municipal electric power systems associated with greenhouse gas control under uncertainty. Khalid and Savkin [23] developed and tested a methodology for controlling the emissions from a group of microgenerators aggregated in a virtual power plant with wind power.…”

Section: Introductionmentioning

confidence: 99%

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A Joint Scheduling Optimization Model for Wind Power and Energy Storage Systems considering Carbon Emissions Trading and Demand Response

Aiwei

Chen

2016

Mathematical Problems in Engineering

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To reduce the influence of wind power random on system operation, energy storage systems (ESSs) and demand response (DR) are introduced to the traditional scheduling model of wind power and thermal power with carbon emission trading (CET). Firstly, a joint optimization scheduling model for wind power, thermal power, and ESSs is constructed. Secondly, DR and CET are integrated into the joint scheduling model. Finally, 10 thermal power units, a wind farm with 2800 MW of installed capacity, and 3 × 80 MW ESSs are taken as the simulation system for verifying the proposed models. The results show backup service for integrating wind power into the grid is provided by ESSs based on their charge-discharge characteristics. However, system profit reduces due to ESSs' high cost. Demand responses smooth the load curve, increase profit from power generation, and expand the wind power integration space. After introducing CET, the generation cost of thermal power units and the generation of wind power are both increased; however, the positive effect of DR on the system profit is also weakened. The simulation results reach the optimum when both DR and CET are introduced.

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Section: Mathematical Model With Cetmentioning

confidence: 99%

Section: Loadmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Joint Scheduling Optimization Model for Wind Power and Energy Storage Systems considering Carbon Emissions Trading and Demand Response

Aiwei

Chen

2016

Mathematical Problems in Engineering

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“…As one of the primary sources of carbon emissions, the power industry has a dramatic potential for carbon emissions reduction and obvious scope for optimization. In the past two decades, numerous studies have been conducted regarding power system planning and dispatching under market circumstances that take into account carbon trading and, in particular, future carbon prices [2][3][4]. Therefore, the development of a reliable carbon price forecasting and analysis approach is the key to anticipating the changing trends of the energy market, and, thus, to provide a valid reference for establishing power industry policy.…”

Section: Introductionmentioning

confidence: 99%

A Carbon Price Forecasting Model Based on Variational Mode Decomposition and Spiking Neural Networks

et al. 2016

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Accurate forecasting of carbon price is important and fundamental for anticipating the changing trends of the energy market, and, thus, to provide a valid reference for establishing power industry policy. However, carbon price forecasting is complicated owing to the nonlinear and non-stationary characteristics of carbon prices. In this paper, a combined forecasting model based on variational mode decomposition (VMD) and spiking neural networks (SNNs) is proposed. An original carbon price series is firstly decomposed into a series of relatively stable components through VMD to simplify the interference and coupling across characteristic information of different scales in the data. Then, a SNN forecasting model is built for each component, and the partial autocorrelation function (PACF) is used to determine the input variables for each SNN model. The final forecasting result for the original carbon price can be obtained by aggregating the forecasting results of all the components. Actual InterContinental Exchange (ICE) carbon price data is used for simulation, and comprehensive evaluation criteria are proposed for quantitative error evaluation. Simulation results and analysis suggest that the proposed VMD-SNN forecasting model outperforms conventional models in terms of forecasting accuracy and reliability.

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“…These papers studied carbon emissions within a certain environment; however, carbon emissions are generally uncertain as they are affected by many factors such as the type and age of the technology and the generating environment. After examination of the relationship between the regional authority, power generation groups and grid companies, Xu et al developed a tripartite equilibrium for carbon emissions allowance allocation in the power-supply industry [17] and Zhu et al developed a full-infinite fuzzy stochastic programming method for planning municipal electric power systems to control greenhouse gases under uncertainty [18]. Following these innovations, in this paper, we also incorporate carbon emissions mitigation concerns into our bi-level power dispatch problem in which the power grid companies consider total carbon emissions minimization as a new objective and the power generation groups utilize carbon trading to achieve greater sustainable growth.…”

Section: Introductionmentioning

confidence: 99%

Low-Carbon Based Multi-Objective Bi-Level Power Dispatching under Uncertainty

et al. 2016

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This research examines a low-carbon power dispatch problem under uncertainty. A hybrid uncertain multi-objective bi-level model with one leader and multiple followers is established to support the decision making of power dispatch and generation. The upper level decision maker is the regional power grid corporation which allocates power quotas to each follower based on the objectives of reasonable returns, a small power surplus and low carbon emissions. The lower level decision makers are the power generation groups which decide on their respective power generation plans and prices to ensure the highest total revenue under consideration of government subsidies, environmental costs and the carbon trading. Random and fuzzy variables are adopted to describe the uncertain factors and chance constrained and expected value programming are used to handle the hybrid uncertain model. The bi-level models are then transformed into solvable single level models using a satisfaction method. Finally, a detailed case study and comparative analyses are presented to test the proposed models and approaches to validate the effectiveness and illustrate the advantages.

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A dynamic model to optimize municipal electric power systems by considering carbon emission trading under uncertainty

Cited by 28 publications

References 34 publications

A Joint Scheduling Optimization Model for Wind Power and Energy Storage Systems considering Carbon Emissions Trading and Demand Response

A Joint Scheduling Optimization Model for Wind Power and Energy Storage Systems considering Carbon Emissions Trading and Demand Response

A Carbon Price Forecasting Model Based on Variational Mode Decomposition and Spiking Neural Networks

Low-Carbon Based Multi-Objective Bi-Level Power Dispatching under Uncertainty

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