Conference Agenda

The transition toward net-zero energy systems implies a rise of distributed generators, batteries, and new consumers, including electric vehicles (EVs) and heat pumps. The additional generation, consumption, and flexibility of these assets may substantially impact wholesale electricity markets. This is, however, subject to distribution gird constraints, which have been neglected in existing wholesale market models. Here, we propose to use the “equivalent electricity storage” approach to aggregate individual consumers’ net load and flexibility at distribution grid level, taking underlying grid constraints into account. The local constraints are approximated based on the installed capacity of low-voltage substations in exemplary distribution grids and scaled to the federal level proportionately to the prevalence of settlement structures. We illustratively apply the approach to flexible electric vehicle charging in Germany for a 2035 scenario. We find that considering distribution grid constraints reduces both the volatility and flexibility of electric vehicle charging, affecting wholesale markets. We analyze further implications for the wholesale market equilibrium as well as the value of relaxing distribution grid constraints.

Electricity market operators worldwide use mixed-integer linear programming to solve the allocation problem in wholesale electricity markets. Prices are typically determined based on the duals of relaxed versions of this optimization problem. The resulting outcomes are efficient, but market operators must pay out-of-market uplifts to some market participants and incur a considerable budget deficit that was criticized by regulators. As the share of renewables increases, the number of market participants will grow, leading to larger optimization problems and runtime issues. At the same time, non-convexities will continue to matter, e.g., due to ramping constraints of the generators required to address the variability of renewables or non-convex curtailment costs. We draw on recent theoretical advances in the approximation of competitive equilibrium to compute allocations and prices in electricity markets using convex optimization. The proposed mechanism promises approximate efficiency, no budget deficit, and computational tractability. We present experimental results for this new mechanism in the context of electricity markets, and compare the runtimes, the average efficiency loss of the method, and the uplifts paid with standard pricing rules. We find that the computations with the new algorithm are considerably faster for relevant problem sizes. In general, the computational advantages come at the cost of efficiency losses and a price markup for the demand side. Interestingly, both are small with realistic problem instances. Importantly, the market operator does not incur a budget deficit and the uplifts paid to market participants are significantly lower compared to standard pricing rules.

Energy storage assets such as hydro pump storages or large scale batteries will assume a pivotal role in the future energy landscape. In contrast to conventional generation assets, which largely operate on fixed schedules and can only vary the amount of energy they provide to the grid, storage assets can be dynamically scheduled in real-time and can both provide energy to and take energy from the grid. As a result, they derive a substantial portion of their monetary value from trading in the highly volatile intraday markets.

This creates many interesting challenges for the purpose of pricing and valuation, risk management, and operational trading.

In this talk, we will explore the case of a single battery energy storage system with the goal of maximizing revenue from trading in the continuous intraday market. The trading problem is framed as a stochastic control problem, where we must account for the simultaneous stochastic evolution of all price processes corresponding to the individual settlement periods underlying a day (which can vary from hours to half hours or quarter hours). The technical specifications and constraints specific to the battery system are incorporated as terminal payoff through the boundary conditions. Together, this results in a high-dimensional stochastic control problem with non-linear and complex boundary conditions. To tackle these challenges, we combine numerical methods with machine learning; specifically, the Deep-FBSDE approach under the stochastic maximum principle as proposed in [Ji, Peng, Peng, and Zhang 2022].