Optimizing production scheduling of steel plate hot rolling for economic load dispatch under time-of-use electricity pricing

Time-of-Use (TOU) electricity pricing provides an opportunity for industrial users to cut electricity costs. Although many methods for Economic Load Dispatch (ELD) under TOU pricing in continuous industrial processing have been proposed, there are still difficulties in batch-type processing since power load units are not directly adjustable and nonlinearly depend on production planning and scheduling. In this paper, for hot rolling, a typical batch-type and energy intensive process in steel industry, a production scheduling optimization model for ELD is proposed under TOU pricing, in which the objective is to minimize electricity costs while considering penalties caused by jumps between adjacent slabs. A NSGA-II based multi-objective production scheduling algorithm is developed to obtain Pareto-optimal solutions, and then TOPSIS based multi-criteria decision-making is performed to recommend an optimal solution to facilitate filed operation. Experimental results and analyses show that the proposed method cuts electricity costs in production, especially in case of allowance for penalty score increase in a certain range. Further analyses show that the proposed method has effect on peak load regulation of power grid.Comment: 13 pages, 6 figures, 4 table

A robust scalable demand-side management based on diffusion-ADMM strategy for smart grid

Demand-side management (DSM) involves a group of programs, initiatives, and technologies designed to encourage consumers to modify their level and pattern of electricity usage. This is performed following methods such as financial incentives and behavioral change through education. While the objective of the DSM is to achieve a balance between energy production and demand, effective and efficient implementation of the program rests within effective use of emerging Internet of things (IoT) concept for online interactions. Here, a novel DSM framework based on diffusion and alternating direction method of multipliers (ADMM) strategies, repeated under a model predictive control (MPC) protocol, is proposed. On the demand side, the customers autonomously and by cooperation with their immediate neighbors estimate the baseline price in real time. Based on the estimated price signal, the customers schedule their energy consumption using the ADMM cost-sharing strategy to minimize their incommodity level. On the supply side, the utility company determines the price parameters based on the customers real-time behavior to make a profit and prevent the infrastructure overload. The proposed mechanism is capable of tracking drifts in the optimal solution resulting from the changes in supply/demand sides. Moreover, it considers all classes of appliances by formulating the DSM problem as a mixed-integer programming (MIP) problem. Numerical examples are provided to show the effectiveness of the proposed framework

Participation of distributed loads in power markets that co-optimize energy and reserves

Thesis (Ph.D.)--Boston UniversityAs the integration of Renewable Generation into today's Power Systems is progressing rapidly, capacity reserve requirements needed to compensate for the intermittency of renewable generation is increasing equally rapidly. A major objective of this thesis is to promote the affordability of incremental reserves by enabling loads to provide them through demand response. Regulation Service (RS) reserves, a critical type of bi-directional Capacity Reserves, are provided today by expensive and environmentally unfriendly centralized fossil fuel generators. In contrast, we investigate the provision of low-cost RS reserves by the demand-side. This is a challenging undertaking since loads must first promise reserves in the Hour Ahead Markets, and then be capable of responding to the dynamic ISO signals by adjusting their consumption effectively and efficiently. To this end, we use Stochastic Control, Optimization Theory, and Approximate Dynamic Programming to develop a decision support framework that assists Smart Neighborhood Operators or Smart Building Operators (SNOs/SBOs) to become demand-side-providers of RS reserve. We first address the SNO/SBO short time scale operational task of responding to the Independent System Operator's (ISO) dynamic RS requests. We start by developing a model-based Markovian decision problem that trades off ISO RS tracking against demand response related utility loss. Starting with a model based approach we obtain near optimal operational policies through a novel approximate policy iteration technique and an actor critic approach which is robust to partial knowledge of the underlying system dynamics. We then abandon the model based terrain and solve the dynamic operational problem through reinforcement learning that is capable of modeling a population of duty cycle appliances with realistic thermodynamics. We finally propose a smart thermostat design and develop an adaptive control policy that can drive the smart thermostat effectively. The latter approach is particularly suited for systems whose dynamics and dynamically changing consumer preferences are not known or observed beyond the total power consumption. We then address the SNO/SBO task of bidding RS reserves to the hour ahead market. This task determines the maximal RS reserves that the SNO/SBO can promise based on information available at the beginning of an hour, so as to maximize the associated hour-ahead revenues minus the expected average operating cost that will be incurred during the operational task to follow. To accomplish this task, we (i) develop probabilistic constraints that model the feasible maximum reserves which can be offered to the market without exceeding the SNO/SBO's ability to later track the unanticipated dynamic ISO RS signal, and (ii) calibrate a describing function that approximates the average operational cost as a function of the maximal reserves that can be feasibly offered in the day ahead market. The above is made possible by statistical analysis of the controlled system's stochastic dynamics and properties of the optimal dynamic policies that we derive. The contribution of the thesis is twofold: The solution of a difficult stochastic control problem that is crucial for effective demand-response-based provision of regulation service, and, the characterization of key properties of the stochastic control problem solution, which allow its integration into the hour-ahead market bidding problem

Integration of Energy Storage into a Future Energy System with a High Penetration of Distributed Photovoltaic Generation

Energy storage units (ESU) are increasingly used in electrical distribution systems because they can perform many functions compared with traditional equipment. These include peak shaving, voltage regulation, frequency regulation, provision of spinning reserve, and aiding integration of renewable generation by mitigating the effects of intermittency. As is the case with other equipment on electric distribution systems, it is necessary to follow appropriate methodologies in order to ensure that ESU are installed in a cost-effective manner and their benefits are realized. However, the necessary methodologies for integration of ESU have not kept pace with developments in both ESU and distribution systems. This work develops methodologies to integrate ESU into distribution systems by selecting the necessary storage technologies, energy capacities, power ratings, converter topologies, control strategies, and design lifetimes of ESU. In doing so, the impact of new technologies and issues such as volt-VAR optimization (VVO), intermittency of photovoltaic (PV) inverters, and the smart PV inverter proposed by EPRI are considered. The salient contributions of this dissertation follow. A unified methodology is developed for storage technology selection, storage capacity selection, and scheduling of an ESU used for energy arbitrage. The methodology is applied to make technology recommendations and to reveal that there exists a cost-optimal design lifetime for such an ESU. A methodology is developed for capacity selection of an ESU providing both energy arbitrage and ancillary services under a stochastic pricing structure. The ESU designed is evaluated using ridge regression for price forecasting; Ridge regression applied to overcome numerical stability and overfitting issues associated with the large number of highly correlated predictors. Heuristics are developed to speed convergence of simulated annealing for placement of distributed ESU. Scaling and clustering methods are also applied to reduce computation time for placement of ESU (or any other shunt-connected device) on a distribution system. A probabilistic model for cloud-induced photovoltaic (PV) intermittency of a single PV installation is developed and applied to the design of ESU