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Production cost minimization (PCM) simulation is commonly employed for assessing the operational efficiency, economic viability, and reliability, providing valuable insights for power system planning and operations. However, solving a PCM problem is time-consuming, consisting of numerous binary variables for simulation horizon extending over months and years. This hinders rapid assessment of modern energy systems with diverse planning requirements. Existing methods for accelerating PCM tend to sacrifice accuracy for speed. In this paper, we propose a stable relay learning optimization (s-RLO) approach within the Branch and Bound (B&B) algorithm. The proposed approach offers rapid and stable performance, and ensures optimal solutions. The two-stage s-RLO involves an imitation learning (IL) phase for accurate policy initialization and a reinforcement learning (RL) phase for time-efficient fine-tuning. When implemented on the popular SCIP solver, s-RLO returns the optimal solution up to 2 times faster than the default relpscost rule and 1.4 times faster than IL, or exhibits a smaller gap at the predefined time limit. The proposed approach shows stable performance, reducing fluctuations by approximately 50% compared with IL. The efficacy of the proposed s-RLO approach is supported by numerical results., Comment: Submitted to IEEE Transactions on Power Systems on December 15, 2023

Electricity price prediction plays a vital role in energy storage system (ESS) management. Current prediction models focus on reducing prediction errors but overlook their impact on downstream decision-making. So this paper proposes a decision-focused electricity price prediction approach for ESS arbitrage to bridge the gap from the downstream optimization model to the prediction model. The decision-focused approach aims at utilizing the downstream arbitrage model for training prediction models. It measures the difference between actual decisions under the predicted price and oracle decisions under the true price, i.e., decision error, by regret, transforms it into the tractable surrogate regret, and then derives the gradients to predicted price for training prediction models. Based on the prediction and decision errors, this paper proposes the hybrid loss and corresponding stochastic gradient descent learning method to learn prediction models for prediction and decision accuracy. The case study verifies that the proposed approach can efficiently bring more economic benefits and reduce decision errors by flattening the time distribution of prediction errors, compared to prediction models for only minimizing prediction errors.

One challenge in the optimization and control of societal systems is to handle the unknown and uncertain user behavior. This paper focuses on residential demand response (DR) and proposes a closed-loop learning scheme to address these issues. In particular, we consider DR programs where an aggregator calls upon residential users to change their demand so that the total load adjustment is close to a target value. To learn and select the right users, we formulate the DR problem as a combinatorial multi-armed bandit (CMAB) problem with a reliability objective. We propose a learning algorithm: CUCB-Avg (Combinatorial Upper Confidence Bound-Average), which utilizes both upper confidence bounds and sample averages to balance the tradeoff between exploration (learning) and exploitation (selecting). We consider both a fixed time-invariant target and time-varying targets, and show that CUCB-Avg achieves $O(\log T)$ and $O(\sqrt{T \log(T)})$ regrets respectively. Finally, we numerically test our algorithms using synthetic and real data, and demonstrate that our CUCB-Avg performs significantly better than the classic CUCB and also better than Thompson Sampling.

This letter proposes a new design of frequency-locked loop (FLL) which is based on synchronous (dq) reference frame instead of stationary ({\alpha}\b{eta}) reference frame. First, a synchronous reference frame FLL (briefly called SRF-FLL0) equivalent to the conventional FLL is proposed. Then the SRF-FLL0 is improved by utilizing the phase error to acquire a better performance. The small-signal modeling and parameter tuning of the improved synchronous reference frame FLL (SRF-FLL) are presented. Finally, the theoretical analysis and experiment results verify the superiority and effectiveness of proposed SRF-FLL., Comment: 4 pages, 6 figures

Geothermal Heat Pump (GHP) systems are heating and cooling systems that use the ground as the temperature exchange medium. GHP systems are becoming more and more popular in recent years due to their high efficiency. Conventional control schemes of GHP systems are mainly designed for buildings with a single thermal zone. For large buildings with multiple thermal zones, those control schemes either lose efficiency or become costly to implement requiring a lot of real-time measurement, communication and computation. In this paper, we focus on developing energy efficient control schemes for GHP systems in buildings with multiple zones. We present a thermal dynamic model of a building equipped with a GHP system for floor heating/cooling and formulate the GHP system control problem as a resource allocation problem with the objective to maximize user comfort in different zones and to minimize the building energy consumption. We then propose real-time distributed algorithms to solve the control problem. Our distributed multi-zone control algorithms are scalable and do not need to measure or predict any exogenous disturbances such as the outdoor temperature and indoor heat gains. Thus, it is easy to implement them in practice. Simulation results demonstrate the effectiveness of the proposed control schemes., Comment: 8 pages, 10 figures, in Proc. of 2017 American Control Conference, the long version. arXiv admin note: text overlap with arXiv:1702.03308

Due to the development of intelligent demand-side management with automatic control, distributed populations of large residential loads, such as air conditioners (ACs) and electrical water heaters (EWHs), have the opportunities to provide effective demand-side ancillary services for load serving entities (LSEs) to reduce the emissions and network operating costs. Most present approaches are restricted to 1) the scenarios involving with efficiently scheduling the large number of appliances in real time, 2) the issues about evaluating the contributions of individual residents towards participating demand response (DR) program, and fairly distributing the rewards, and 3) the concerns on performing cost-effective demand reduction request (DRR) for LSEs with minimal rewards costs while not affecting their living comfortableness. Therefore, this paper presents an optimal framework for residential load aggregators (RLAs) which helps solve the problems mentioned above. Under this framework, RLAs are able to realize the DRR for LSEs to generate optimal control strategies over residential appliances quickly and efficiently. To residents, the framework is designed with probabilistic model of comfortableness, which minimizes the impact of DR program to their daily life. To LSEs, the framework helps minimize the total reward costs of performing DRRs. Moreover, the framework fairly and strategically distributes the financial rewards to residents, which may stimulate the potential capability of loads optimized and controlled by RLAs in demand side management. The proposed framework has been validated on several numerical case studies., Comment: to be submitted to IEEE Trans. on Smart Grid