Photovoltaic Power Producer Research Articles

Research on the Dynamic Characteristics of Photovoltaic Power Production and Sales Based on Game Theory

This paper mainly studies the dynamic characteristics of production and sales of distributed photovoltaic power. This is beneficial for the construction and development of a green power system, and it promotes the sustainable development of the social economy. First, the production and sales laws of the photovoltaic power are analyzed, and the trade process between photovoltaic power producers (PVPs) and photovoltaic power demanders (PVDs) is modeled as a game. Different from the existing relevant literature, two kinds of payoff bimatrices are provided, which correspond to the PVD market and PVP market, respectively. Then, the dynamic characteristics of the model are revealed by using the semitensor product method. The results present a more reliable theoretical basis for the sustainable development of the green electricity market. Finally, through an illustrative example, it can be seen that the strategies of all participants are constantly changing to obtain more profits rather than stable at a traditional Nash equilibrium point. It is worth pointing out that the method and results are applicable to other distributed low-carbon energies, contributing to the development of sustainable energy systems.

Optimization Bidding Strategy on Day Ahead and Balancing Market of PV Generator Considering Battery Integration

AbstractTo realize Carbon Neutrality, renewable energy would be the main source of electricity, such as photovoltaic (PV) power generation in the Japanese power system. However, a decrease in the number of operating thermal generators, the main source of balancing power in the Japanese balancing market, is causing concerns regarding a shortage of balancing power in the future. PV can therefore provide balancing power in addition to energy; thus, the PV power economic value in electricity markets will become diversified. A solution examination and rationality analysis of technical issues for PV‐based balancing power creation are needed. Here, by applying the proposed headroom control method based on the PV output curtailment beforehand, PV generator optimizes benefit by bidding into the balancing market when the spot market price is very low. Optimizing the bidding strategy on the spot market and balancing market is conducted, and battery integration is also considered. The optimization simulation results validate the possibility of creating balancing power by a PV power producer, and the total revenue valuation verifies a more beneficial effect. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Robust Optimization Model for Photovoltaic Power Producer’s Bidding Decision-Making in Electricity Market

With the deepening of electricity market (EM) reform and the high penetration of photovoltaic (PV) energy in power system, the uncertainties of a PV power output and fluctuation of EM prices would bring substantial financial risks for PV power producers (PPPs). This paper proposed a robust optimization model for PPP’s power bidding decision-making. Specifically, the random PV power outputs are modeled by the uncertainty set, which need no probabilistic information and can fully depict the continuous range of uncertainties. Subsequently, with respect to any scenario for day-ahead EM prices, PPP’s optimal power bidding strategy is obtained under the worst-case realization within the uncertainty set, which guarantees the robustness in resisting the negative impact of random PV power outputs on PPP’s profit. Moreover, a reformulation approach was introduced for equivalently transforming our model into a tractable framework. Simulation was implemented to validate the feasibility and effectiveness of applying our proposed model.

Participation of photovoltaic power producers in short-term electricity markets based on rescheduling and risk-hedging mapping

Optimal bidding that considers different electricity market floors can increase the financial gains of photovoltaic (PV) power producers. However, the current approach to trading PV power essentially consists of committing to sell the forecasted PV generation. To analyze profits and investigate new business opportunities for PV power producers, this paper proposes two novel stochastic programming-based methods for scheduling and rescheduling for trading the PV generated energy in day-ahead and intraday electricity markets. Risk-hedging is also considered in terms of co-optimizing the expected profit with the Conditional Value-at-Risk (CVaR) metric. As a consequence of the structure and organization of the market floors and due to different market windows, rescheduling is necessary to exploit the most recent information. Updated rescheduling progressively reveals actual profits or losses, risk-hedging possible engagement in business transactions, and the final effect of strategic bidding. A case study in the Spanish electricity market based on actual data is presented. The analysis of the case study shows the influence of the three market floors (day-ahead, intraday, and imbalance), the participation in multiple intraday sessions, risk-hedging, and rescheduling on the profits of the PV producer.

Operation Strategy of Shared ESS Based on Power Sensitivity Analysis to Minimize PV Curtailment and Maximize Profit

Nowadays, due to the renewable energy policy, the penetration of photovoltaic (PV) has increased, but the curtailment of PV output power has also increased. However, for PV power producers, who earn profit from selling power to the utility, this kind of curtailment will lead to a decline in their profit. Thus it is necessary to determine how to perform proper curtailment for them. This study proposes a shared energy storage system (ESS) operation strategy based on the power flow at the points of common coupling (PCC) and the power sensitivity of the line loss effect between buses where each PV power producer is located. The proposed algorithm maximizes the overall regional profit through arbitrage trading of shared ESS and performs a curtailment that reduces the effect of line loss at the PCC points. The performance verification of the proposed algorithm is conducted through a simulation on MATLAB, the benefits of shared ESS and the economic feasibility of reducing the curtailment are also analyzed. Moreover, the results of shared ESS benefits and curtailment allocations based on power sensitivity are analyzed for each PV power producer. The results show an improved local profit and PCC power flow.

Effect of Prediction Error of Machine Learning Schemes on Photovoltaic Power Trading Based on Energy Storage Systems

Photovoltaic (PV) output power inherently exhibits an intermittent property depending on the variation of weather conditions. Since PV power producers may be charged to large penalties in forthcoming energy markets due to the uncertainty of PV power generation, they need a more accurate PV power prediction scheme in energy market operation. In this paper, we characterize the effect of PV power prediction errors on energy storage system (ESS)-based PV power trading in energy markets. First, we analyze the prediction accuracy of two machine learning (ML) schemes for the PV output power and estimate their error distributions. We propose an efficient ESS management scheme for charging and discharging operation of ESS in order to reduce the deviations between the day-ahead (DA) and real-time (RT) dispatch in energy markets. In addition, we estimate the capacity of ESSs, which can absorb the prediction errors and then compare the PV power producer’s profit according to ML-based prediction schemes with/without ESS. In case of ML-based prediction schemes with ESS, the ANN and SVM schemes yield a decrease in the deviation penalty by up to 87% and 74%, respectively, compared with the profit of those schemes without ESS.

Life‐cycle loss evaluation of power transformers serving large photovoltaic plants in vertically integrated and decentralised systems

This study details a comprehensive loss evaluation method of power transformers serving large-scale solar applications. The fact that these transformers are obliged to serve an intermittent energy source calls for a suitable method to evaluate their life-cycle losses and total ownership costs. These transformers may be owned by independent photovoltaic power producers or by regulated utilities. Thus, the method concurrently responds to the current efforts to address the concept of loss evaluation both in vertically-integrated and decentralised energy systems that are experiencing a high penetration of renewable energy.