Optimal Dispatch of a Virtual Power Plant Considering Demand Response and Carbon Trading

Liu, Zuoyu; Zheng, Weimin; Qi, Feng; Wang, Lei; Zou, Bo; Wen, Fushuan; Xue, Yuncan

doi:10.3390/en11061488

Cited by 48 publications

(33 citation statements)

References 33 publications

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“…To ensure human comfort within an acceptable range, the present work applies the following constraint to the regulation and control processes of CACSs based on σ PMV [37]: -0.5 ≤ σ PMV ≤ 0.5 (1) The relationship between σ PMV and the indoor temperature T in can be expressed as follows [38,39]:…”

Section: Comfort Index Of the Human Bodymentioning

confidence: 99%

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Stochastic Adaptive Robust Dispatch for Virtual Power Plants Using the Binding Scenario Identification Approach

et al. 2019

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The present study establishes a stochastic adaptive robust dispatch model for virtual power plants (VPPs) to address the risks associated with uncertainties in electricity market prices and photovoltaic (PV) power outputs. The model consists of distributed components, such as the central air-conditioning system (CACS) and PV power plant, aggregated by the VPP. The uncertainty in the electricity market price is addressed using a stochastic programming approach, and the uncertainty in PV output is addressed using an adaptive robust approach. The model is decomposed into a master problem and a sub-problem using the binding scenario identification approach. The binding scenario subset is identified in the sub-problem, which greatly reduces the number of iterations required for solving the model, and thereby increases the computational efficiency. Finally, the validity of the VPP model and the solution algorithm is verified using a simulated case study. The simulation results demonstrate that the operating profit of a VPP with a CACS and other aggregated units can be increased effectively by participating in multiple market transactions. In addition, the results demonstrate that the binding scenario identification algorithm is accurate, and its computation time increases slowly with increasing scenario set size, so the approach is adaptable to large-scale scenarios.

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Section: Comfort Index Of the Human Bodymentioning

confidence: 99%

“…A CACS is generally composed of chillers that produce cold energy Q ch t and thermal storage tanks for the storage and release of cold energy, which are denoted as Q st t and Q re t , respectively. Accordingly, Q t can be given as follows [39]:…”

Section: Central Air-conditioning System Modelmentioning

confidence: 99%

Stochastic Adaptive Robust Dispatch for Virtual Power Plants Using the Binding Scenario Identification Approach

et al. 2019

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“…The second aims at the implementation of a national carbon market, which represents a breakthrough for the electricity sector. The addition of a carbon price to the traditional thermal unit generation cost, which makes coal-fired power plants much less competitive, will drive rapid growth in renewable and gas-fired generation, supplanting coal-fired power and producing significant environmental benefits [31][32][33][34].…”

Section: Structure and Assumptionsmentioning

confidence: 99%

Scenario Analysis in the Electric Power Industry under the Implementation of the Electricity Market Reform and a Carbon Policy in China

Wang

2019

Energies

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In China, electricity market reform was first implemented in 2015. At the same time, the national carbon market was built, along with the electricity industry, which was considered a breakthrough. Some key considerations for the future development of China's electricity system include the implementation of demand-side measures in order to adjust the peak-to-valley difference and the economic dispatch of increasing intermittent renewable energy and traditional energy in the process of power marketization with the implementation of a carbon policy. This paper examines the impact of policies on electricity generation by modelling the evolution process of power marketization and the economic dispatch of generation technologies over a sixteen-year period beginning in 2020. We model four potential influencing factors of government policy: (1) the demand response mode;(2) power marketization process; (3) capacity adjustment of thermal power units; and (4) carbon taxes, which vary in terms of their timing and amount. This model assesses the impact of these influencing factors on the competition between electricity generators using a range of output variables, including generation portfolios, electricity prices, capacity factors, CO 2 emissions, etc. The results show that the new round of electricity market reforms has had a positive impact on renewable energy generation. The influence of carbon policy is evident in the promotion, transformation and elimination of thermal units, and an indirect increase in renewable energy generation.Energies 2019, 12, 2152 4 of 26 is considered, and (4) the power sector pays the carbon cost of thermal power units under the implementation of the carbon market.An illustrative model structure of power generation economic analysis is presented in Figure 1, including the data input, model design scenarios, objective functions and model output. The data input requires data on the detailed composition of the power sector at the beginning of the planning horizon, predicted power demand, future installed capacity development plan, CO 2 prices, as well as the technological and fuel parameters, considered over the planning horizon. The modus of China's future electricity generation is divided into two parts, under electricity marketization, in this model: the first part is the "equal shares" dispatch generation for the planned electricity sector, whereby all types of power generation technologies comply with the "equal shares" electricity generation rules; the second part entails the "economic optimization" dispatch generation for the electricity market sector, which is under an economic optimization algorithm that selects the lowest-cost option for electricity generation. The output of the model can provide detailed information about the pathway of China's power sector under different policy scenarios over the planning horizon, including the power generation profiles, capacity factors of various power generation technologies, market price on an hourly basis, costs and revenues of various generatio...

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“…Recently, the concept of VPP technology has attracted extensive attention in many industries, both domestically and internationally. In foreign countries, from 2001 to 2005, Germany, The Netherlands, and Spain jointly established the virtual fuel cell power plant program, consisting of 31 residential fuel cell combined heat and power (CHP) systems [4]. From 2005 to 2009, the United Kingdom, Spain, and France established a flexible electricity network in order to integrate the expected energy solution (FENIX) project [5] for the sustainable development of the EU power supply.…”

Section: Introductionmentioning

confidence: 99%

A CVaR-Robust Risk Aversion Scheduling Model for Virtual Power Plants Connected with Wind-Photovoltaic-Hydropower-Energy Storage Systems, Conventional Gas Turbines and Incentive-Based Demand Responses

Tan

et al. 2018

Energies

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To make full use of distributed energy resources to meet load demand, this study aggregated wind power plants (WPPs), photovoltaic power generation (PV), small hydropower stations (SHSs), energy storage systems (ESSs), conventional gas turbines (CGTs) and incentive-based demand responses (IBDRs) into a virtual power plant (VPP) with price-based demand response (PBDR). Firstly, a basic scheduling model for the VPP was proposed in this study with the objective of the maximum operation revenue. Secondly, a risk aversion model for the VPP was constructed based on the conditional value at risk (CVaR) method and robust optimization theory considering the operating risk from WPP and PV. Thirdly, a solution methodology was constructed and three cases were considered for comparative analyses. Finally, an independent micro-grid on an industrial park in East China was utilized for an example analysis. The results show the following: (1) the proposed risk aversion scheduling model could cope with the uncertainty risk via a reasonable confidence degree β and robust coefficient Γ. When Γ ≤ 0.85 or Γ ≥ 0.95, a small uncertainty brought great risk, indicating that the risk attitude of the decision maker will affect the scheduling scheme of the VPP, and the decision maker belongs to the risk extreme aversion type. When Γ ∈ (0.85, 0.95), the decision-making scheme was in a stable state, the growth of β lead to the increase of CVaR, but the magnitude was not large. When the prediction error e was higher, the value of CVaR increased more when Γ increased by the same magnitude, which indicates that a lower prediction accuracy will amplify the uncertainty risk. (2) when the capacity ratio of (WPP, PV): ESS was higher than 1.5:1 and the peak-to-valley price gap was higher than 3:1, the values of revenue, VaR, and CVaR changed slower, indicating that both ESS and PBDR can improve the operating revenue, but the capacity scale of ESS and the peak-valley price gap need to be set properly, considering both economic benefits and operating risks. Therefore, the proposed risk aversion model could maximize the utilization of clean energy to obtain higher economic benefits while rationally controlling risks and provide reliable decision support for developing optimal operation plans for the VPP.

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Optimal Dispatch of a Virtual Power Plant Considering Demand Response and Carbon Trading

Cited by 48 publications

References 33 publications

Stochastic Adaptive Robust Dispatch for Virtual Power Plants Using the Binding Scenario Identification Approach

Stochastic Adaptive Robust Dispatch for Virtual Power Plants Using the Binding Scenario Identification Approach

Scenario Analysis in the Electric Power Industry under the Implementation of the Electricity Market Reform and a Carbon Policy in China

A CVaR-Robust Risk Aversion Scheduling Model for Virtual Power Plants Connected with Wind-Photovoltaic-Hydropower-Energy Storage Systems, Conventional Gas Turbines and Incentive-Based Demand Responses

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