Optimal Power Scheduling Problem Research Articles

A Fast Algorithm for Optimal Power Scheduling of Large-Scale Appliances With Temporally Spatially Coupled Constraints

Optimally scheduling power consumption of appliances is the essential feature of smart grid, which enables Demand Response Management (DRM) and helps to shape the power usage profile. This problem is often required to be solved in face of a large number of appliances and many time slots; thus the computational efficiency of solving a large scale optimal power scheduling problem with limited computational resources becomes the major concern of algorithm design. To this end, a novel algorithm is proposed based on Karush-Kuhn-Tucker (KKT) conditions to solve the optimal power scheduling problem with temporally spatially coupled constraints in a distributed manner. The proposed algorithm converts the original problem into equivalently solving an optimal KKT operator in a much lower dimension, thus the computation speed is greatly enhanced. In addition, the proposed method dose not require a step size in the iteration process, thus avoids the oscillation of numerical solution caused by problem parameter changes. Compared with the widely used conventional algorithms, e.g., interior point method and dual decomposition, the higher computational speed and less sensitivity to the problem parameter setting are observed in numerical simulations.

A novel two-model based approach for optimal scheduling in CSP plants

Thermal energy storage (TES) systems allow concentrated solar power (CSP) producers to participate in a day-ahead market. Then, the optimal power scheduling problem can be posed, whose objective is the maximisation of profits derived from electricity sales. Most papers in literature use a mixed-integer linear programming (MILP) approach to solve this type of problems. This paper proposes a novel approach based on the use of two models: a detailed model and a MILP model. This approach combines MILP capabilities and the accuracy of a detailed model. The proposed approach is applied to a 50MW parabolic-trough-collector based CSP plant with molten-salt-based TES. A detailed model available in literature and validated against operating plant data is used, but some improvements are included for its use in optimal scheduling problems. Moreover, the MILP model was developed to adjust as much as possible to the features of the detailed model. The improvements regarding other scheduling strategies for a specific example are shown.

A MPC approach for optimal generation scheduling in CSP plants

Thermal energy storage (TES) systems allow concentrated solar power (CSP) producers to participate in a day-ahead market. Therefore, the optimal power scheduling problem can be posed, whose objective is the maximization of profits derived from electricity sales. The daily generation schedule has to be offered in advance, usually the previous day before a certain time, thus an electricity price and weather forecast must be carried out. This paper proposes a model-based predictive control (MPC) approach for optimal scheduling in CSP plants. This approach has a dual purpose: (1) the periodic update of the generation schedule to track the schedule that has been committed to by means of the most recent electricity price and weather forecast and information about the plant state and (2) the generation of the optimal schedule for the next day. As these two tasks are related, they are performed simultaneously. Therefore, the MPC sliding window is composed of a first time interval to track the committed schedule and a second time interval to generate the next schedule for the following hours. This is then offered as the generation schedule for the next day at the appropriate time. The proposed approach is applied, in a simulation context, to a 50MW parabolic trough collector-based CSP plant with molten-salt-based TES. The chosen criterion to track the committed schedule is the even distribution of the possible generation error within the first interval. A case-study with overestimated initial DNI forecast is undertaken. The results show that the MPC control with short-term DNI forecast significantly improves the above-mentioned objective and allows for a reduction of the deviation from the scheduled generation, when compared with the case without short-term DNI forecast.

Stochastic Effects of Renewable Energy and Loads on Optimizing Microgrid Market Benefits

Abstract This paper investigates the effects of uncertain renewable energy and loads on optimizing profit and cost in a microgrid power market. The optimal power scheduling problem is solved using interval arithmetic backward forward sweep (IA-BFS) and particle swarm optimization with time varying acceleration coefficients (PSO-TVAC) based optimal power flow (OPF). The effectiveness of the problem and the method is verified by studying the deviations in dispatch of conventional sources, operational cost and overall profit in residential feeder of the CIGRE LV benchmark microgrid with load curtailment, grid trade and wind, solar & conventional energy sources.

Power scheduling of universal decentralized estimation in sensor networks

We consider the optimal power scheduling problem for the decentralized estimation of a noise-corrupted deterministic signal in an inhomogeneous sensor network. Sensor observations are first quantized into discrete messages, then transmitted to a fusion center where a final estimate is generated. Supposing that the sensors use a universal decentralized quantization/estimation scheme and an uncoded quadrature amplitude modulated (QAM) transmission strategy, we determine the optimal quantization and transmit power levels at local sensors so as to minimize the total transmit power, while ensuring a given mean squared error (mse) performance. The proposed power scheduling scheme suggests that the sensors with bad channels or poor observation qualities should decrease their quantization resolutions or simply become inactive in order to save power. For the remaining active sensors, their optimal quantization and transmit power levels are determined jointly by individual channel path losses, local observation noise variance, and the targeted mse performance. Numerical examples show that in inhomogeneous sensing environment, significant energy savings is possible when compared to the uniform quantization strategy.

Effects of hydro models on optimal operation of variable head hydro-thermal systems

The problem of optimum short-range scheduling of an integrated-hydro-thermal electric power system with variable head hydro plants is considered. The scheduling problem involves the solution of the coordination for an arbitrary hydro plant performance model. Of the many models available, the Glimn-Kirchmayer model and the Hamilton-Lamont model are selected for use in deriving optimality conditions for the problem. The Newton-Raphson method is the basis of an algorithm developed solving the problem of optimal power scheduling iteratively. The authors report on the effect of the form of the hydro plant model on the resulting optimal scheduling. Several test systems are used to examine model effects in terms of optimal fuel cost, thermal and hydro generation and transmission losses. Effects of the models on the convergence properties of the algorithm and a comparison of execution times are also reported.

OPTIMAL ECONOMIC OPERATION OF THERMAL AND VARIABLE HEAD HYDRO SYSTEMS USING HAMILTON-LAMONT'S MODEL AND NEWTON-RAPHSON METHOD

ABSTRACT In this paper, optimum scheduling of an integrated hydro-thermal electric power systems with variable head hydro plants, is treated. The scheduling problem is solved by the use of coordination equations developed by Ricard and Kron. There are many reported models to represent the performance of a hydro plant, and here Hamilton-Lamont's model is used to derive specific optimality conditions for the problem. Using Newton-Raphson method, an algorithm is developed for solving iteratively the problem of optimal power scheduling. The iterative method is implemented using a specially designed initial guess estimator based on the physical properties of the problem to generate a set of feasible initial estimates of the unknowns to speed up the process. A number of example systems are used to test the performance of the algorithm. Successful convergence of the algorithm is reported in this paper and results for one sample test system are given.