| The introduction of virtual trading in wholesale electricity markets allows market members to arbitrage by taking advantage of expected price differences between the day-ahead energy market and the real-time energy market.Virtual trading can interact with other elements of the wholesale electricity market,and market members may covertly manipulate virtual trading to improperly profit in other related markets.Thus,virtual trading is both an important market design and a risk for regulators to confront.However,there has been relatively little research on cross-market manipulation by market members through virtual trading.In this paper,we consider the relationship between virtual trading and the value of financial transmission rights,and develop a bi-level model of cross-market manipulation in the electricity market considering virtual trading.In the upper-level model,the manipulator’s investment cap,the ceiling of single-node virtual trading under the regulatory constraint and the single-node virtual trading form constraint are considered to decide the virtual trading strategy with the goal of maximizing the profit of cross-market manipulation.The lower-level model is the day-ahead market single-time clearing model,which takes the lowest cost of generation for the whole society as the objective function and the safe and stable operation of the grid as the constraint to determine the real clearing volume of virtual generation and the day-ahead market clearing price in each scenario,and uses multiple scenarios to express the uncertainty of network blockage.In the bi-level optimization model of this paper,the variables of the upper model are parameters in the lower model,which is a linear programming,so the Kuhn Tuck optimality condition is used to replace the lower model,transform the bi-level optimization model into a single-level mathematical programming model with equilibrium constraints,and transform the MPEC model into a mixed integer linear programming model using the pairwise condition,Big M method.The created MILP model is solved by programming and invoking the CPLEX optimization solver through the General Algebraic Modeling System.Through two-node,five-node and thirty-node network arithmetic examples and result analysis,the effects of network blocking,virtual trading strategies and financial transmission rights investment strategies on cross-market manipulation are analyzed,and the corresponding regulatory principles and regulatory methods are proposed,thus verifying the effectiveness of the model and solution methods proposed in this paper. |
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