Power Scheduling Problem Research Articles

Matpower-Based Harmonic Power Flow Analysis for Power Systems With Passive Power Filters

As an open-source MATLAB-based power system simulation package, MATPOWER has been proven to have an adjustable feature that supports modifications to solve specific problems in power systems. Since it was first released, the MATPOWER framework has been widely used for solving steady-state electric power scheduling problems. However, a modification to solve a harmonic power flow (HPF) using MATPOWER has not been launched yet. This paper presents modification procedures to customize the network component models and problem formulations in MATPOWER for the HPF study. The modification procedures can be used to evaluate the bus admittance matrix, bus voltages, and current sources for any harmonic order. Moreover, the modified MATPOWER can evaluate the passive power filter (PPF) installation to improve the harmonic distortion in power systems. The simulation results were satisfactory, indicating that the modification enabled to solve problems in both fundamental and harmonic power flows.

A new flexible and resilient model for a smart grid considering joint power and reserve scheduling, vehicle-to-grid and demand response

The optimal, resilient and flexible operation of the power system is one of the most important challenges of the power grid. Fast resources as a backup capacity to provide power are one of the ways the power system to be more flexible and resilient. In this paper, a new joint power and reserve scheduling is done considering scheduling based on power instead of scheduling based on energy, the correct management of the thermal units’ ramp, the use of responsive loads includes a collection of commercial customers and plug-in electric vehicles (PEVs) in the secondary (15-minute) and tertiary (30-minute) reserves and discharge management of PEVs. A part of the power demand of these responsive loads is provided as free by renewable resources in times when there are no bad weather conditions. The use of responsive loads, correct management of the ramp of thermal units and a part of the discharge of PEVs can help restore the network quickly. The mixed-integer linear programming (MILP) method has been used to solve the joint power and reserve scheduling problem. The results show that the proposed model can lead to more flexibility and resiliency of the system and reduce the cost of the system.

A novel hybrid grey wolf optimizer with min-conflict algorithm for power scheduling problem in a smart home

Abstract In this paper, the min-conflict local search algorithm (MCA) is hybridized with the grey wolf optimizer (GWO) for the power scheduling problem in smart home (PSPSH), and the proposed method is called GWO-MCA. MCA is utilized as a new operator of GWO to improve its exploitation capability in addressing constraint satisfaction problems, particularly scheduling problems. GWO is one of the most efficient metaheuristic algorithms which mimics the hunting behavior of grey wolves. PSPSH is a problem of scheduling smart home appliances in accordance with a dynamic pricing scheme(s) to flatten users’ power consumption. PSPSH’s objectives are to reduce electricity bills, improve user comfort, and maintain power systems’ performance. PSPSH is formulated as a multi-objective optimization problem to achieve all objectives simultaneously. GWO-MCA is tested by using up to 36 appliance operations for 7 days. To show the effect of MCA on the convergence behavior of optimization problems, MCA is utilized as a new operator on other five popular optimization methods: genetic algorithm, particle swarm optimization, wind-driven optimization, antlion optimizer, and enhanced differential evolution. Interestingly, MCA shows a high impact on the performance of such algorithms. In addition, GWO-MCA achieves a better schedule than all compared MCA-Based methods in tackling PSPSH. In addition, the results of GWO-MCA are compared with those of three state-of-the-art hybrid methods to verify the GWO-MCA performance. The GWO-MCA excels the other comparative methods in almost all datasets used. Besides, the GWO-MCA method is compared with 20 state-of-the-art methods using their datasets. Again, GWO-MCA is able to outperform them for almost all datasets used.

Optimal Sensor and Relay Nodes Power Scheduling for Remote State Estimation with Energy Constraint.

We study the sensor and relay nodes’ power scheduling problem for the remote state estimation in a Wireless Sensor Network (WSN) with relay nodes over a finite period of time given limited communication energy. We also explain why the optimal infinite time and energy case does not exist. Previous work applied a predefined threshold for the error covariance gap of two contiguous nodes in the WSN to adjust the trade-off between energy consumption and estimation accuracy. However, instead of adjusting the trade-off, we employ an algorithm to find the optimal sensor and relay nodes’ scheduling strategy that achieves the smallest estimation error within the given energy limit under our model assumptions. Our core idea is to unify the sensor-to-relay-node way of error covariance update with the relay-node-to-relay-node way by converting the former way of the update into the latter, which enables us to compare the average error covariances of different scheduling sequences with analytical methods and thus finding the strategy with the minimal estimation error. Examples are utilized to demonstrate the feasibility of converting. Meanwhile, we prove the optimality of our scheduling algorithm. Finally, we use MATLAB to run our algorithm and compute the average estimation error covariance of the optimal strategy. By comparing the average error covariance of our strategy with other strategies, we find that the performance of our strategy is better than the others in the simulation.

Power Flow Management in Multi-Source Electric Vehicle Charging Station

Grid-tied renewable energy sources (RES) with battery-behind-meter (BBM) architectures have successfully been used to ensure effective energy cooperation between the grid and RES-based microgrids. Such environments are quite stochastic, thus making power management very challenging. This paper presents the use of an asynchronous Q-learning in performing a power flow management task in a multi-source electric vehicle charging station with the integration of vehicle-to-microgrid technology. The power scheduling problem is first formulated as a Markov decision process. Asynchronous Q-learning is then used to solve it. The algorithm is tested with a typical charging station load profile over a 24-hour period and compared with a simple rule-based algorithm. Simulation results show that the proposed method is able to select a power schedule that reduces the energy cost with a better utilization of both the battery storage system and the vehicle to microgrid energy compared to the rule-based method.

MPC-Based Distributed Control for Intelligent Energy Management of AC Microgrids

Power scheduling of distributed storage devices and renewable energy resources in microgrids is crucial for their reliable and optimal operation. Conventionally, the power scheduling problem is solved with a centralized energy management control, which has access to all the information from the overall system. This type of control has limitations related to computational burden, communication latencies, and missing information. Therefore, the primary objective of this paper is to propose a distributed energy management strategy, to reduce the operational cost of microgrids, and to enhance the computation time. The strategy is formulated based on the model predictive control method since it allows the control action in the current time-step to be optimized while taking into account the future time-steps. Outputs of the energy management system are the optimal power references generated for all energy resources based on the system’s demand, constraints, and objective functions. To illustrate the effectiveness of the distributed model predictive control, the proposed algorithm is validated through simulation, on a microgrid candidate via MATLAB/Simulink, and controller hardware-in-the-loop experiment on a LabVIEW platform using National Instruments control devices (myRIO). The results will show the computation time in the distributed IEM method is improved compared to centralized IEM. Additionally, the results will show the robustness of the distributed IEM in the plug and play scenario.

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.

Probabilistic day-ahead simultaneous active/reactive power management in active distribution systems

Distributed generations (DGs) are main components for active distribution networks (ADNs). Owing to the large number of DGs integrated into distribution levels, it will be essential to schedule active and reactive power resources in ADNs. Generally, energy and reactive power scheduling problems are separately managed in ADNs. However, the separate scheduling cannot attain a global optimum scheme in the operation of ADNs. In this paper, a probabilistic simultaneous active/reactive scheduling framework is presented for ADNs. In order to handle the uncertainties of power generations of renewable-based DGs and upstream grid prices in an efficient framework, a stochastic programming technique is proposed. The stochastic programming can help distribution system operators (DSOs) to make operation decisions in front of existing uncertainties. The proposed coordinated model considers the minimization of the energy and reactive power costs of all distributed resources along with the upstream grid. Meanwhile, a new payment index as loss profit value for DG units is introduced and embedded in the model. Numerical results based on the 22-bus and IEEE 33-bus ADNs validate the effectiveness of the proposed method. The obtained results verify that through the proposed stochastic-based power management system, the DSO can effectively schedule all DGs along with its economic targets while considering severe uncertainties.

Hierarchical optimisation strategy for energy scheduling and volt/var control in autonomous clusters of microgrids

This study proposes a hierarchical optimisation strategy for the energy dispatch and volt/var control problem of a photovoltaic-battery microgrid cluster (MGC) operating autonomously. The proposed approach takes advantage of the decentralised control architecture existing in multi-microgrids (MMGs) framework by distributing the management responsibilities between the microgrid central controllers (MGCCs) and the central autonomous management controller (CAMC). In the first stage, the optimisation strategy solves a multi-temporal active power scheduling problem for the MGC based on consumption and generation forecasts. In the second stage, the reactive power and volt/var control are addressed by taking into account the medium-voltage (MV) and low-voltage levels independently. For this purpose, each MGCC computes the V(Q) capability area of operation at the boundary bus with the MV grid. Then, the CAMC performs an optimal power flow at the MV level for each time step, whose results at the boundary bus are considered in the last stage to schedule reactive power at the MGCC level. The effectiveness of the proposed strategy is demonstrated in a cluster of three microgrids. It keeps the modularity, interoperability and scalability characteristics of the MMG concept by clearly defining the roles and the information to be exchanged between the CAMC and the MGCC.

Offloading Delay Constrained Transparent Computing Tasks With Energy-Efficient Transmission Power Scheduling in Wireless IoT Environment

Billions of lightweight Internet of Things (IoT) devices have been deployed for various applications nowadays. Most of them first collect interested data and then process them in some degree according to application requirements. Transparent computing (TC) is a promising technique that makes such lightweight devices suitable to process even large-size applications. The advantage of TC is to separate code storage from its execution, allowing IoT devices to load code blocks from nearby TC storage server on demand. Distinct from existing work, this paper allows the TC IoT devices to offload some tasks to servers, since wireless IoT devices are usually powered by batteries, having limited energy resources. If a task is offloaded, a challenging problem is that its input data collected by the IoT device must be transferred as well, which incurs additional transmission time and energy. This paper proposes a two-step approach aiming at minimizing the energy consumption of the IoT device while satisfies the delay constraint. This approach first studies the offloading decision problem that determines for each task whether to offload task data or load task code blocks, while loading code indicates code receiving and executing energy cost. Second, the transmission power scheduling problem is investigated to further reduce offloading energy for a given delay constrained offloading task set. Heuristic decision making algorithms and optimal power scheduling algorithm are proposed, respectively. Such two-step approach is shown by extensive simulation to be near optimal for the original problem thanks to the optimal design of the power scheduling algorithm.

Economic and environmental analysis of a co-generation power system with the incorporation of renewable energy resources

Abstract Combined heat and power is utilized to generate electric power and produce thermal energy for various applications. In view of this, we investigate the economic savings and environmental effects of using a co-generation system that is made-up of microturbine (MT) and other auxiliaries instead of using only a gas boiler. This paper addresses the power-scheduling problem among the generating units in a microgrid (MG) system that comprises of photovoltaic (PV), wind turbine generator (WTG), MT, grid and battery storage system (BSS). The optimal operation of the proposed MG system is obtained by considering the demand response, operating costs and other key performance indicators. The objective function of this study is to minimize the cost of energy, NPC, emission, operating cost, fuel cost and grid energy purchase. HOMER software is utilized to accomplish the goal of the research work. In this paper, a model is developed to investigate the performances of the system having considered the economic and environmental significance of utilizing renewable energy resources (RERs) in a MG system. The results of the simulation analysis reveal that the proposed model is cost effective to reduce the operating costs of the MG system with the application of RERs.

Power optimization of the environmental control system for the civil more electric aircraft

As the civil aviation industry moves toward to the more-electric-aircraft (MEA) concept, environmental control system (ECS), one of the largest power consumption non-propulsive systems for civil aircraft, converts its power extraction source from bleed air to electric power and thus will introduce new challenges on ECS's design and operation management schemes. One of these challenges is for the power optimization of ECS, which was an optimal sizing and planning problem for ECS air circulation pack, but under the MEA environment will be transformed into a complex power scheduling problem integrated with the constraints on the controllable electrical power generation and varying thermal power demands determined by the aircraft's flight profiles. In this paper, we propose a complete mathematical optimization formulation for the power optimization of ECS for MEA and cast it as a non-convex nonlinear program. We provide a solution approach to tackle this problem based on a combination of Benders decomposition method and the successively linear programming technique and illustrate the capabilities of the proposed solution approach on an aircraft's ECS under the civil MEA environment. The numerical results demonstrate the effectiveness of the proposed solution approach in terms of the associated computational outcomes.

Multi-objective power scheduling problem in smart homes using grey wolf optimiser

In this paper, the multi-objective grey wolf optimiser is utilised for the power scheduling problem (PSP). The grey wolf optimiser (GWO) is a recent swarm-based optimisation algorithm tailored for various optimisation problems. PSP is addressed by scheduling home appliances to a certain time horizon to minimise the electricity bill and peak-to-average ratio (PAR) and increase the comfort level of users. The multi-objective function is formalised and utilised in GWO to obtain an optimal schedule. Seven consumption profiles and seven real-time electricity prices with various characteristics are considered to evaluate the proposed multi-objective GWO. The performance of the proposed algorithm is tested against three factors, namely, electricity bill, PAR and user comfort level. The obtained schedule shows that all evaluation factors are optimally timetabled. For a comparative evaluation, the proposed method is firstly compared with the genetic algorithm. The proposed method exhibits and yield better performance than GA under the same consumption profiles. Secondly, the proposed method is compared with 19 state-of-the-art methods by using the recommended consumption profiles of these methods and their evaluation criteria. The proposed method nearly outperforms the compared methods in terms of minimisation of electricity bill and PAR. User comfort level is a criterion proposed in this study and has not been considered previously. It exerts a significant impact on the final schedule.

Distributed Resource Scheduling Based on Potential Game in Dense Cellular Network

Based on a potential game model, a distributed resource scheduling scheme is proposed to coordinate severe interference in a dense cellular network. First, the subcarrier and power scheduling problem is modeled as a non-cooperative potential game. Then, individual utility and overall potential functions are proposed by considering attainable throughput and consumed power. According to the definition of the potential game, the difference in individual utilities caused by strategy changing has the same value as the difference in overall potential value, which guarantees that players can maximize respective utility without worsening system profit. Afterward, an iterative searching algorithm is designed to obtain optimal resource scheduling strategy. Theoretical analysis and simulation results both validate that the proposed scheduling scheme makes a performance improvement in spectral and resource efficiency. Meanwhile, the proposed scheme can also guarantee the quality of service by providing superior user signal to interference plus noise ratio.

Stochastic dynamic programming approach to managing power system uncertainty with distributed storage

Wind integration in power grids is challenging because of the uncertain nature of wind speed. Forecasting errors may have costly consequences. Indeed, power might be purchased at highest prices to meet the load, and in case of surplus, power may be wasted. Energy storage may provide some recourse against the uncertainty of wind generation. Because of their sequential nature, in theory, power scheduling problems may be solved via stochastic dynamic programming. However, this scheme is limited to small networks by the so-called curse of dimensionality. This paper analyzes the management of a network composed of conventional power units and wind turbines through approximate dynamic programming, more precisely stochastic dual dynamic programming. A general power network model with ramping constraints on the conventional generators is considered. The approximate method is tested on several networks of different sizes. The numerical experiments also include comparisons with classical dynamic programming on a small network. The results show that the combination of approximation techniques enables to solve the problem in reasonable time.

Optimal Reactive Power Scheduling Using Cuckoo Search Algorithm

<p>This paper solves an optimal reactive power scheduling problem in the deregulated power system using the evolutionary based Cuckoo Search Algorithm (CSA). Reactive power scheduling is a very important problem in the power system operation, which is a nonlinear and mixed integer programming problem. It optimizes a specific objective function while satisfying all the equality and inequality constraints. In this paper, CSA is used to determine the optimal settings of control variables such as generator voltages, transformer tap positions and the amount of reactive compensation required to optimize the certain objective functions. The CSA algorithm has been developed from the inspiration that the obligate brood parasitism of some Cuckoo species lay their eggs in nests of other host birds which are of other species. The performance of CSA for solving the proposed optimal reactive power scheduling problem is examined on standard Ward Hale 6 bus, IEEE 30 bus, 57 bus, 118 bus and 300 bus test systems. The simulation results show that the proposed approach is more suitable, effective and efficient compared to other optimization techniques presented in the literature.</p>

Reliability-aware low energy scheduling in real time systems with shared resources

Dynamic voltage scaling (DVS) is a technique which is widely used to save energy in a real time system. Recent research shows that it has a negative impact on the system reliability. In this paper, we consider the problem of the system reliability and focus on a periodic task set that the task instance shares resources. Firstly, we present a static low power scheduling algorithm for periodic tasks with shared resources called SLPSR which ignores the system reliability. Secondly, we prove that the problem of the reliability-aware low power scheduling for periodic tasks with shared resources is NP-hard and present two heuristic algorithms called SPF and LPF respectively. Finally, we present a dynamic low power scheduling algorithm for periodic tasks with shared resources called DLPSR to reclaim the dynamic slack time to save energy while preserving the system reliability. Experimental results show that the presented algorithm can reduce the energy consumption while improving the system reliability.

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.

Residential power scheduling for demand response in smart grid

Abstract This paper studies the power scheduling problem for residential consumers in smart grid. In general, the consumers have two types of electric appliances. The first type of appliances have flexible starting time and work continuously with a fixed power. The second type of appliances work with a flexible power in a predefined working time. The consumers can adjust the starting time of the first type of appliances or reduce the power consumption of the second type of appliances to reduce the payments. However, this will also incur discomfort to the consumers. Assuming the electricity price is announced by the service provider ahead of time, we propose a power scheduling strategy for the residential consumers to achieve a desired trade-off between the payments and the discomfort. The power scheduling is formulated as an optimization problem including integer and continuous variables. An optimal scheduling strategy is obtained by solving the optimization problem. Simulation results demonstrate that the scheduling strategy can achieve a desired tradeoff between the payments and the discomfort.