Abstract
This research acquired data from the Central Weather Bureau Observation Data Inquiry System (CODIS) for historical weather information, such as observation time, temperature, humidity, wind speed, global radiation, etc., and constructed a historical weather database by using Excel software. Least square support vector machine (LSSVM) was used to forecast wind speed and solar radiation; then, the power output of wind and solar was derived. Considering factors of the demand response and the load and electricity pricing, a maximized risk income model of the virtual power plant (VPP) is established based on conditional value-at-risk (CVAR). An enhanced bacterial foraging algorithm (EBFA) was proposed to solve the risk dispatch problem of a VPP in this paper. In an EBFA, the stochastic weight trade-off is embedded to improve the behavior pattern of individual bacteria to enhance their sorting efficiency and accuracy in a high-dimension solution space. Various moving patterns of EBFA were considered for improvement, which were demonstrated by using a VPP system on Penghu island, Taiwan. Many scenarios were created, including various seasons, power rebate pricings, and confidence levels, so the maximal risk and return of VPP could be simulated and analyzed. Simulation and tests show a positive result for a VPP to perform the power dispatch by maximizing risk income. This paper also provides a guideline for the VPP to handle the risk management.
Highlights
Climate change due to the greenhouse effect is a major topic in studying the increasingly serious problem of global warming
Utility is transparent to the virtual power plant (VPP), which needs the data of power output only, while the utility may have to deal with many VPPs
This paper developed an enhanced bacterial foraging algorithm (EBFA) algorithm to solve the risk dispatch of a VPP to obtain maximal profit
Summary
Climate change due to the greenhouse effect is a major topic in studying the increasingly serious problem of global warming. Based on the demand response strategy, the demand bidding mechanism of the VPP was used in the electricity market [12,13,14] to help participants obtain profit Some approaches, such as the binding scenario identification approach [15], mixed integer quadratic programming [16], deep learning-based prediction and particle swarm optimization [17], etc., were used to solve the operation and dispatch problem of VPPs. In the above studies, VPP problems were addressed by considering the risks without the related uncertainties, i.e., with the concept of a “deterministic” risk, regardless of the fact that uncertainties are inherent in risk. This paper provides a guideline for planning a VPP and includes risk management
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