Optimal Load Scheduling Research Articles

Residential Community Micro Grid Load Scheduling and Management System Using Cooperative Game Theory

This paper proposes a residential community based microgrid using cooperative game theory to share excessive energy within a community’s neighbor homes for optimal load scheduling and management. The proposed model is a grid connected residential community where smart homes are connected through central energy management system (EMS) to share the benefits of excessive distributed energy resources (DERs) from solar PV or wind turbine by selling to other community residents at a price lower than the utility gird but higher than the feed–in tariff. The community smart homes are categorized as Externally Importing Homes, Internally Exporting Homes and Externally Exporting Homes which are further classified as passive consumers, active prosumers and proactive prosumers based on the facilities they possess in form of DERs and battery storage (BS). With the cooperative energy transaction mechanism, the smart community homes after fulfilling their own load requirements can place the excessive energy on community poll using decentralized or centralized approach through peer to peer trading or smart community manager (SCM) respectively. The excessive energy can be sold or purchased to and from other community homes as per some defined preferences and priorities. This will benefit the entire community in terms of cost compared to the utility grid’s Time of Use (ToU) pricing. Proposed system will not only share, schedule and manage the community load optimally but will reduce the overall energy cost, system operational stress, improves system operational efficiency and reduces carbon emission.

Optimal load scheduling of household appliances considering consumer preferences: An experimental analysis

This paper discusses an experimental study of the home appliances scheduling problem that incorporates realistic aspects. The residential load scheduling problem is solved while considering consumer’s preferences. The objective function minimizes the weighted sum of electricity cost by earning relevant incentives, and the scheduling inconvenience. The objective of this study is five-fold. First, it sought to develop and solve a binary integer linear programming optimization model for the problem. Second, it examined the factors that might affect the obtained schedule of residential loads. Third, it aimed to test the performance of a developed optimization model under different experimental scenarios. Fourth, it proposes a conceptual definition of a new parameter in the problem, the so-called “flexibility ratio”. Finally, it adds a data set for use in the literature on the home appliance scheduling problem, which can be used to test the performance of newly-developed approaches to the solution of this problem. This paper presents the results of experimental analysis using four factors: problem size, flexibility ratio, time slot length and the objective function weighting factor. The experimental results show the main and interaction effects, where these exist, on three performance measures: the electricity cost, inconvenience and the optimization model computation time.

Cloudy GSA for load scheduling in cloud computing

Scheduling of load and data plays an important role in the efficient utilization of the resources from one cloudlet to another cloudlet in the cloud computing environment. Cloud computing is an incremental paradigm to brighten the world with its great vision of providing the power of distributed computing through virtual approach. Resource allocation plays an important role in the optimal handling of the load scheduling problem using static and meta-heuristic approaches. The Gravitational Search Algorithm (GSA) is a nature-inspired meta-heuristic optimization technique which is used for solving the load scheduling problem in the cloud computing environment and is based on Newton’s gravitational law dealing with gravity. This paper proposes a near optimal load scheduling algorithm named Cloudy-GSA to minimize the transfer time and the total cost incurred in scheduling the cloudlets to the VMs. These are achieved by increased exploitation of VMs using the particles based on fitness values. The Cloudy-GSA algorithm is implemented on the CloudSim and has been compared with the existing popular algorithms. The results of the algorithm are converged and statistically analysed over a set of iterations. As evident from the results, the proposed Cloudy-GSA algorithm minimizes the transfer time and the total cost for scheduling the load than the existing algorithms.

Storage trade-offs and optimal load scheduling for cooperative consumers in a microgrid with different load types

ABSTRACTGrowing demand and aging infrastructure has put the current electricity grid under increased pressure. Microgrids (μGs) equipped with storage are believed to be the future of electricity grids that will be able to achieve energy efficiency by integrating renewable energy sources. Storage can be used to mitigate the time-varying and intermittent nature of renewable energy sources. In this article, we consider optimal load scheduling in a μG for four different load types: production line loads, non-moveable loads, time moveable loads, and modifiable power loads for different types of consumers. Consumers cooperate with the System Operator to schedule their loads to achieve overall energy efficiency in the μG. Two different options for charging the storage are considered: (i) charging from excess harvest in μG and (ii) charging from the Macrogrid. We perform sensitivity analysis on the storage capacity for two pricing policies to understand its trade-offs with the total electricity cost and Peak to Average Ratio. Computational experiments with different problem instances demonstrate that: (i) charging storage from the Macrogrid allows higher flexibility in load scheduling; and (ii) load scheduling with cooperative consumers outperforms the individualistic and random scheduling in terms of total electricity cost.

Optimal Load Scheduling of a Smart Home Supplied by a Photovoltaic Grid-Connected Plant

Energy management of a household connected to the grid, but with a local PV installation is studied here. For this a new formulation is proposed, that takes into account the PV generation, the grid energy cost, the priority of extensions and the requested level of user comfort. A multi-start random constructive heuristic algorithm is proposed to optimize the scheduling of a smart home supplied by a Photovoltaic- grid connected plant. The proposed approach is tested on a specific case study, in two different scenarios depending on the comfort value of the user. The obtained results show the efficiency of the proposed approach.

Model-based optimal load control of inverter-driven air conditioners responding to dynamic electricity pricing

Dynamic electricity pricing provides a great opportunity for residential consumers to participate into demand response (DR) programs to reduce the electricity bills. The lack of automatic response to time-varying electricity prices is one of the challenges faced by the residential electric appliances. Most of the existing studies on DR of residential air conditioner (ACs) focus on the single-speed ACs, rather than the inverter-driven ACs which are more energy efficient and extensively installed in today’s residential buildings. This paper presents a novel model-based optimal load scheduling method for residential inverter-driven ACs to realize automatic DR to the day-ahead dynamic electricity prices. The models of the inverter-driven ACs and room thermal dynamics are firstly developed, identified and integrated for the development of the model-based scheduling. The tradeoff problem between the electricity costs, resident’s comfort and peak power reductions is formulated as a mixed-integer nonlinear programming problem with adjustable weightings. The optimal solution of the nonlinear programming problem is searched by the genetic algorithm (GA). Simulation results show that multiple goals can be achieved via GA optimization and the regulations of the weights in the objective function. The developed framework can be implemented in the programmable communicating thermostats (PCTs) or the smart home energy manage systems (HEMSs) to enable residential inverter-driven ACs automatically respond to day-ahead dynamic electricity pricing.

Optimal Load Scheduling of Plug-In Hybrid Electric Vehicles via Weight-Aggregation Multi-Objective Evolutionary Algorithms

In order to protect the environment and slow down global warming trend, many governments and environmentalists are keen at promoting the use of plug-in hybrid electric vehicles (PHEVs). As a result, more and more PHEVs have been put into use. However, load peak caused by their disordered charging can be detrimental to an entire power grid. Several methods have been proposed to establish ordered PHEV charging. While focusing on single-objective load scheduling, they fail to meet the real requirements that need one to conduct multiple objective optimization. This paper formulates a multi-objective load scheduling problem to minimize two competing objectives: 1) potential serious peak-to-valley difference and 2) economic loss. When we apply existing multi-objective evolutionary algorithms (MOEAs), i.e., multi-objective particle swarm optimization (MOPSO), Nondominated Sorting Genetic Algorithm II, MOEA based on decomposition, and multi-objective differential evolutionary algorithm to solve it, because its high dimension and special conditions we find that they fail to reach the Pareto Front or converge into a relatively small area only. Therefore, we propose a weight aggregation (WA) strategy and implement a novel MOEA algorithm named WA-MOPSO by incorporating WA into MOPSO to solve the problem. Its effectiveness and efficiency to generate a Pareto front of this problem are verified and compared with those of the state-of-the-art approaches. Furthermore, WA is also combined with other MOEAs to solve the defined scheduling problem.

Investigation of demand response potentials of residential air conditioners in smart grids using grey-box room thermal model

Over the last few years, the development of information and communication technologies has provided a great opportunity for the residential sector to take part in demand response (DR) programs in smart grids (SGs). Optimal load scheduling via home energy management systems (HEMSs) is a typical technique used to reduce the power consumptions during the DR events. One of the major challenges faced by the HEMS manufacturers and the electric utilities is the lack of an accurate yet convenient tool for predicting the power consumptions of residential homes, particularly the air conditioners, for decision-makings. The aim of this paper is to develop an accurate self-learning grey-box room thermal model and use it to investigate DR potentials of residential air conditioners (ACs). The readily available indoor air and outdoor air temperatures in today’s HEMSs are used to train the room thermal model. The model parameters are pre-estimated and scaled to improve the optimization accuracy and computational efficiency. Three optimization techniques including trust region algorithm (TRA), genetic algorithm (GA) and particle swam optimization (PSO) are employed to identify the model parameters separately and their performances are compared. A case study shows that the room thermal model can accurately predict the indoor air temperature profile. Two types of DR strategies of residential ACs, i.e. temperature set-point reset and precooling, are then tested using the room thermal model and a simplified air conditioner energy model. Simulation results show that temperature set-point reset combined with precooling strategy can result in more than 26% power reduction during the DR hours on a typical summer day in Hong Kong, without significant change of thermal comfort.

An optimal day-ahead load scheduling approach based on the flexibility of aggregate demands

The increasing trends of energy demand and renewable integration call for new and advanced approaches to energy management and energy balancing in power networks. Utilities and network system operators require more assistance and flexibility shown from consumers in order to manage their power plants and network resources. Demand response techniques allow customers to participate and contribute to the system balancing and improve power quality. Traditionally, only energy-intensive industrial users and large customers actively participated in demand response programs by intentionally modifying their consumption patterns. In contrast, small consumers were not considered in these programs due to their low individual impact on power networks, grid infrastructure and energy balancing. This paper studies the flexibility of aggregated demands of buildings with different characteristics such as shopping malls, offices, hotels and dwellings. By using the aggregated demand profile and the market price predictions, an aggregator participates directly in the day-ahead market to determine the load scheduling that maximizes its economic benefits. The optimization problem takes into account constraints on the demand imposed by the individual customers related to the building occupant comfort. A case study representing a small geographic area was used to assess the performance of the proposed method. The obtained results emphasize the potential of demand aggregation of different customers in order to increase flexibility and, consequently, aggregator profits in the day-ahead market.

Optimal switchable load sizing and scheduling for standalone renewable energy systems

The variability of solar energy in off-grid systems dictates the sizing of energy storage systems along with the sizing and scheduling of loads present in the off-grid system. Unfortunately, energy storage may be costly, while frequent switching of loads in the absence of an energy storage system causes wear and tear and should be avoided. Yet, the amount of solar energy utilized should be maximized and the problem of finding the optimal static load size of a finite number of discrete electric loads on the basis of a load response optimization is considered in this paper. The objective of the optimization is to maximize solar energy utilization without the need for costly energy storage systems in an off-grid system. Conceptual and real data for solar photovoltaic power production provides the power input to the off-grid system. Given the number of units, the following analytical solutions and computational algorithms are proposed to compute the optimal load size of each unit: mixed-integer linear programming and constrained least squares. Based on the available solar power profile, the algorithms select the optimal on/off switch times and maximize solar energy utilization by computing the optimal static load sizes. The effectiveness of the algorithms is compared using one year of solar power data from San Diego, California and Thuwal, Saudi Arabia. It is shown that the annual system solar energy utilization is optimized to 73% when using two loads and can be boosted up to 98% using a six load configuration.

Iterative learning for optimal residential load scheduling in smart grid

In this paper, as a fundamental problem in smart grid, the residential load scheduling is studied in a comprehensive way. The main contributions lie in threefold. First, three indices, i.e., the power consumption expense, the robustness of schedule subject to uncertain electricity price and the satisfaction of customer, are taken into full consideration. We propose to optimize simultaneously the three indices via convex optimization. Second, iterative learning is utilized to setting parameters in the objective function, and keeps a proper tradeoff between the consumption expense and the satisfaction index. Third, in order to fully characterize the operation states of appliances, both binary and continuous variables are used, which results in a hybrid mixed-integer quadratic optimization problem. The relaxation technique is utilized to tackle the hybrid optimization problem. Theoretical analysis of the performance gap based on the proposed approach is provided as well. The performance of the proposed approach is illustrated by simulations. The parameter settings reflect actual preference and consumption manner of the consumer. In addition, both peak-to-average ratio of power load and variation of power load are reduced.

Residential Demand Response Scheduling With Multiclass Appliances in the Smart Grid

In this paper, we study an electricity load scheduling problem in a residence. Compared with previous works in which only limited sets of appliances are considered, we classify various appliances into five sets considering their different energy consumption and operation characteristics, and provide mathematical models for them. With these appliance models, we propose an electricity load scheduling algorithm that controls the operation time and energy consumption level of each appliance adapting to time-of-use pricing in order to maximize the overall net utility of the residence while satisfying its budget limit. The optimization problem is formulated as a mixed integer nonlinear programming (MINLP) problem, which is in general, difficult to solve. In order to solve the problem, we use the generalized Benders decomposition approach with which we can solve the MINLP problem easily with low computational complexity. By solving the problem, we provide an algorithm to obtain the optimal electricity load scheduling of various appliances with different energy consumption and operation characteristics in a unified way.

Incentive-compatible demand-side management for smart grids based on review strategies

Demand-side load management is able to significantly improve the energy efficiency of smart grids. Since the electricity production cost depends on the aggregate energy usage of multiple consumers, an important incentive problem emerges: self-interested consumers want to increase their own utilities by consuming more than the socially optimal amount of energy during peak hours since the increased cost is shared among the entire set of consumers. To incentivize self-interested consumers to take the socially optimal scheduling actions, we design a new class of protocols based on review strategies. These strategies work as follows: first, a review stage takes place in which a statistical test is performed based on the daily prices of the previous billing cycle to determine whether or not the other consumers schedule their electricity loads in a socially optimal way. If the test fails, the consumers trigger a punishment phase in which, for a certain time, they adjust their energy scheduling in such a way that everybody in the consumer set is punished due to an increased price. Using a carefully designed protocol based on such review strategies, consumers then have incentives to take the socially optimal load scheduling to avoid entering this punishment phase. We rigorously characterize the impact of deploying protocols based on review strategies on the system’s as well as the users’ performance and determine the optimal design (optimal billing cycle, punishment length, etc.) for various smart grid deployment scenarios. Even though this paper considers a simplified smart grid model, our analysis provides important and useful insights for designing incentive-compatible demand-side management schemes based on aggregate energy usage information in a variety of practical scenarios.

Optimal Load Scheduling in Smart Grid Considering Environmental Factors

This paper introduced the research status of microgrid control system(MCS) and analyzed the functions of microgrid control center(MCC). A design of multi-functional of microgrid control center was presented , and each relevant functional module was described.

Optimal Hourly Scheduling of Community-Aggregated Electricity Consumption

This paper presents the optimal scheduling of hourly consumption in a residential community (community, neighborhood, etc.) based on real-time electricity price. The residential community encompasses individual residential loads, communal (shared) loads, and local generation. Community-aggregated loads, which include residential and communal loads, are modeled as fixed, adjustable, shiftable, and storage loads. The objective of the optimal load scheduling problem is to minimize the community-aggregated electricity payment considering the convenience of individual residents and hourly community load characteristics. Limitations are included on the hourly utility load (defined as community-aggregated load minus the local generation) that is imported from the utility grid. Lagrangian relaxation (LR) is applied to decouple the utility constraint and provide tractable subproblems. The decomposed subproblems are formulated as mixed-integer programming (MIP) problems. The proposed model would be used by community master controllers to optimize the utility load schedule and minimize the community-aggregated electricity payment. Illustrative optimal load scheduling examples of a single resident as well as an aggregated community including 200 residents are presented to show the efficiency of the proposed method based on real-time electricity price.

Bidirectional Energy Trading and Residential Load Scheduling with Electric Vehicles in the Smart Grid

Electric vehicles (EVs) will play an important role in the future smart grid because of their capabilities of storing electrical energy in their batteries during off-peak hours and supplying the stored energy to the power grid during peak hours. In this paper, we consider a power system with an aggregator and multiple customers with EVs and propose novel electricity load scheduling algorithms which, unlike previous works, jointly consider the load scheduling for appliances and the energy trading using EVs. Specifically, we allow customers to determine how much energy to purchase from or to sell to the aggregator while taking into consideration the load demands of their residential appliances and the associated electricity bill. We propose two different approaches: a collaborative and a non-collaborative approach. In the collaborative approach, we develop an optimal distributed load scheduling algorithm that maximizes the social welfare of the power system. In the non-collaborative approach, we model the energy scheduling problem as a non-cooperative game among self-interested customers, where each customer determines its own load scheduling and energy trading to maximize its own profit. In order to resolve the unfairness between heavy and light customers in the non-collaborative approach, we propose a tiered billing scheme that can control the electricity rates for customers according to their different energy consumption levels. In both approaches, we also consider the uncertainty in the load demands, with which customers' actual energy consumption may vary from the scheduled energy consumption. To study the impact of the uncertainty, we use the worst-case-uncertainty approach and develop distributed load scheduling algorithms that provide the guaranteed minimum performances in uncertain environments. Subsequently, we show when energy trading leads to an increase in the social welfare and we determine what are the customers' incentives to participate in the energy trading in various usage scenarios including practical environments with uncertain load demands.

Optimal load scheduling within a microgrid including reliability aspects

Establishment and running of microgrids involve considerable investment. In order to justify the investment, the economic benefits arising out of microgrid have to be studied. In this work, an analysis of profit to the loads is performed, including reliability parameters like probability of interruption and average duration of interruption. A case study is presented, for a microgrid operating along with the main grid having a time of day tariff for power supply. When there is interruption of main grid supply, the microgrid generators meet the load. The loads are scheduled such that the probability weighted average electricity cost is the minimum. Then the profit for the loads because of the microgrid is calculated. It is inferred from the simulation results that, the microgrid is beneficial to the loads, subject to reliability conditions, tariff structure and worth of energy for the loads.

Multi-Layer Optimization for Load Scheduling to Manage Unreliable Grid Outages in Developing Countries

This paper describes the significant cost saving opportunities for consumers in developing countries by the use of computational intelligence and demand-side-management techniques to mitigate the massive use of diesel back-up during grid outages. Application of load scheduling optimization is investigated during scheduled power outages, for residential consumer in India. The specific load shifting approaches explored include a day ahead predicted load schedule which is generated by performing a DSM referring to the forecasted day ahead outage. Whereas in reality the predicted may not match the actual outage, thus in these cases a fuzzy logic rule base is referred on real time basis to take corrective action & reach the best optimal load schedule possible to attain the lowest cost. The load types modeled include passive loads and schedulable, i.e. typically heavy loads. It is found that this multi-level DSM schemes show excellent benefits to the consumer. The maximum diesel savings for the consumer due to load shifting can be approximately ranging from 45% to as high as 75% for a flat-tariff grid. The study also showed that the actual savings potential depends on the timing of power outage, duration and the specific load characteristics.

SIMPLE PERFORMANCE BOUNDS FOR MULTICORE AND PARALLEL CHANNEL SYSTEMS

A simple modification of existing divisible load scheduling algorithms, boosting link speed by M for M parallel channels per link, allows time optimal load scheduling and performance prediction for parallel channel systems. The situation for multicore models is more complex but can be handled by a substitution involving equivalent processor speed. These modifications yield upper bounds on such parallel systems' performance. This concept is illustrated for ideal single level (star) tree networks under a variety of scheduling policies. Less than ideal parallelism can also be modeled though mechanisms of inefficiency require further research.

PLC-based Load Management in Steel Rolling Mills

ABSTRACT This article presents a physically-based load model and formulation for continuous process industries to use in implementing industrial load management. The formulation utilizes an integer programming technique for minimizing electricity costs by scheduling the loads and satisfying the process, production, and maximum demand constraints. The case study of a typical steel rolling mill, with the proposed model, shows that savings of about 5.21% in electricity costs are possible with optimal load scheduling under TOU tariff. After scheduling the loads, real time implementation of load management action is investigated using a programmable logic controller.