Analysis of infeasible unit-commitment solutions arising in energy optimization

The day-ahead problem of finding optimal dispatch and prices for the Brazilian power system is modeled as a mixed-integer problem, with nonconvexities related to fixed costs and minimal generation requirements for some thermal power plants. The computational tool DESSEM is currently run by the independent system operator, to define the dispatch for the next day in the whole country. DESSEM also computes marginal costs of operation that CCEE, the trading chamber, uses to determine the hourly prices for energy commercialization. The respective models sometimes produce an infeasible output. This work analyzes theoretically those infeasibilities, and proposes a prioritization to progressively resolve the constraint violation, in a manner that is sound from the practical point of view. Pros and cons of different mathematical formulations are analyzed. Special attention is put on robustness of the model, when the optimality requirements for the unit-commitment problem vary.

An Optimal Dispatch Framework of Electric and Heating Networks Based on Controllable Electric and Thermostatically Controlled Loads

With the increasing capacity of wind power generators (WTGs), the volatility of wind power could significantly challenge the stability and economy of electric and heating networks. To tackle this challenge, this paper proposes an optimal dispatch framework based on controllable load (including controllable electric load and controllable thermostatically load) to reduce wind power curtailment. A forecasting model is developed for the controllable load, which comprehensively considers autocorrelation, weather factor, and consumers’ behavior characteristics. With adjusting controllable load, an optimal dispatch model of power system is then established and resolved by Sequential Least Squares Programming (SLSQP) method. Our method is verified through numerous simulations. The results show that, compared with the state-of-the-art techniques of support vector machine and recurrent neural networks, the root mean square error with the proposed long short-term memory can be reduced by 0.069 and 0.044, respectively. Compared with conventional method, the peak wind power curtailment with dispatching controllable load is reduced by nearly 10% and 5% in two cases, respectively.