Tuning of renewable energy bids based on energy risk management: Enhanced microgrids with pareto‐optimal profits for the utility and prosumers

Abstract

The increasing penetration of renewable energy sources (RES) and electric vehicles (EVs) demands the building of a microgrid energy portfolio that is cost‐effective and robust against generation uncertainties (energy risk). Energy risk may trigger financial risk in the local energy market, depending on bid values, cost of generation and price of upstream grid power. In this study, a microgrid energy portfolio is built based on adjustments to both the financial and energy risks. These risks are managed in two ways: (1) by pre‐tuning and prioritizing the bid prices for wind and solar energy sources based on their relative levels of energy risk as quantified through a conditional value‐at‐risk (CVaR) approach; and (2) by co‐optimizing the conflicting profits of the utility and prosumers using non‐dominated sorting particle swarm optimization (NSPSO) to obtain a risk‐adjusted Pareto‐optimal energy mix. Thus, the utility predicts the net power balancing cost from the scheduling time horizon, thereby moderating the adverse effect that the uncertainties in renewable energy could have on the collective welfare. The proposed method is tested on a grid‐connected CIGRE low‐voltage (LV) benchmark microgrid with solar and wind sources, microturbines, and EVs. The results demonstrate that the obtained portfolio is realistic, welfare‐optimized and cost‐efficient.

Tail risk adjusted clean energy portfolios in P2P transactive markets using Rachev ratio

“…A suitable energy portfolio for P2P settlement shall have a reasonable risk-return trade-off with respect to energy as well as profit, which could minimize the disparity between forecasted and real-time energy outputs to some extent. Many a time, energy portfolio selection is carried out in the literature using tools extended from the models used in financial portfolio selection [30][31][32][33][34][35]. The simplest way of measuring risk is by using standard deviation (SD) or variance (variations on both sides of the forecast), which is the crux of commonly used methods like the Sharpe ratio [31] and Markowitz efficient frontier (EF) [32,33].…”

“…The simplest way of measuring risk is by using standard deviation (SD) or variance (variations on both sides of the forecast), which is the crux of commonly used methods like the Sharpe ratio [31] and Markowitz efficient frontier (EF) [32,33]. However, semi-deviations below the forecast are also quantified sometimes to model the risk in portfolio returns (energy or profit) using the Sortino ratio [35], Markowitz mean semivariance theory [36] and Conditional Value at Risk [34,37]. The authors in [38] and [39] have built energy portfolios using fuzzy membership functions to incorporate uncertainties in renewable generation and load demand while maximizing the total profit and minimizing carbon emissions.…”