Going for Derivatives or Forwards? Minimizing Cashflow Fluctuations of Electricity Transactions on Power Markets

Abstract

In a competitive electricity market, both electricity retailers and generators predict future prices and volumes and execute electricity delivery contracts through power exchange. In such circumstances, they may suffer from uncertainties caused by fluctuations in spot prices and future demand due to their high volatility. In this study, we develop a unified approach using derivatives and forwards on the spot electricity price and weather data to mitigate the cashflow fluctuation for power utilities. We aim to clarify the applicability of our proposed methods and provide a new and useful perspective on hedging schemes involving various electricity utilities, such as power retailers, solar photovoltaic (PV) generators, and thermal generators. Moreover, we analyze the risk of risk takers (such as the insurance companies in this study) in the derivatives market. In addition, we perform empirical simulations to measure out-of-sample hedging effects on their cashflow management using actual data in Japan.

Construction of Mixed Derivatives Strategy for Wind Power Producers

“…In this context, we provided a methodology to construct a derivative portfolio of weather and energy derivatives using nonparametric regression techniques for power retailers and photovoltaic (PV) generators [34]. This idea has been extended to the case where power retailers and generators (PV or thermal generators) trade through the central exchange market, and an insurance company handles weather and electricity derivatives or forward contracts between them [36]. In this study, we extend our previous work to the hedging problem of wind power producers and formulate mixed derivatives by specifying various types of payoff functions.…”

“…Note that the payoff functions are extracted from the estimated regression Equations in ( 2)-( 4), and the difference between the predicted and realized values for V t S t provides out-of-sample hedge errors. Like our previous study in [32,36], we use the following variance reduction rates (VRRs) and normalized mean absolute errors (NMAEs) to evaluate the hedge performance in the out-of-sample case:…”

“…In other words, considering the "net payoff" of a derivative contract is the payoff at the maturity time minus the (fair) price at the contract time, and the essential issue of effective derivative design is to optimize this net payoff under fair trading conditions (i.e., expected net payoff equals zero). This derivative design was first applied in [32] for designing temperature derivatives in electric utilities and has been developed into various forms of derivatives transactions for prediction error losses [33], simultaneous hedging of price and volume [34,35], and mediated hedge trading model among different electric utility players [36]. A common feature of these studies is to use nonparametric regression techniques and to seek derivatives with arbitrarily nonlinear payoffs (nonparametric derivatives) for optimal hedging.…”

“…Other related studies dealing with simultaneous price and volume hedging problems have been conducted for retailers [29][30][31]38], solar power producers [39], wind power producers [40], etc., but all of them deal with derivatives having a single underlying asset (e.g., electricity prices or temperatures). While some of our previous studies have also constructed multiple derivative portfolios for solar power producers and retailers [34,36], this study has a different objective in that it provides useful insights for wind power risk management practices and modeling challenges based on actual wind power generation data of wind farms (WFs) and other observation data for the electricity market and weather. In the context of a simultaneous hedging of price and volume, the quanto option, which has both of them as underlying assets, could be utilized (see, e.g., [41,42]).…”

Effectiveness and Feasibility of Market Makers for P2P Electricity Trading

“…If the same amounts are executed for selling and buying, that is, a position is established between the two markets, then the price difference would be the profit for this strategy. Next, electricity and weather derivatives (including forwards/futures) may be considered practical applications of derivatives theory for real businesses in electricity markets [30][31][32][33][34][35][36][37][38][39][40][41][42][43]. Among them, Yamada and Matsumoto (2021) [41] and [42,43] advocated weather derivatives, the payments of which depend on weather data at a predetermined place and time.…”