Optimal Energy Consumption Scheduling Research Articles

Agent-Based Energy Consumption Scheduling for Smart Grids: An Auction-Theoretic Approach

The future smart grid would help to benefit both the users and the electricity providing companies from smart pricing techniques. In addition, smart pricing can be used to achieve social objectives and would in turn fluctuate wholesale market into demand side. Collecting abundant information regarding the users electricity consumption pattern is a challenging task for utility providing companies. That is, users may not be willing to expose their indigenous information without any incentive. In this paper an Optimal Energy Consumption Scheduling (OECS) mechanism is proposed to tackle this problem. An agent-based forecasting method is designed, which is capable of predicting energy consumption of each consumer with a lead-time of one hour. This forecasting is exploited to estimate the cost of buying required amount of energy from multiple suppliers. Consequently, based on the estimated required energy and cost, an auction mechanism is proposed to optimize the energy traded between consumers and multiple suppliers within a smart grid. The objectives include increased efficiency and cost reduction of electricity usage by the end users. The results and properties of the proposed OECS mechanism are studied, and it is shown that the auction technique is budget balanced for distribution of electrical energy among consumers from diverse renewable generation resources. Extensive numerical simulations are also conducted to show and prove the beneficial properties of OECS mechanism.

Optimal energy flow control strategy for a residential energy local network combined with demand-side management and real-time pricing

Due to the increasing importance of demand-side resources in the energy internet, this paper proposes an optimal energy flow control strategy for a residential energy local network (RELN), which consists of a small number of households, based on the idea of demand-side management. In particular, a type of optimal and dynamic RELN energy consumption scheduling framework is formulated to minimize the daily total operation cost, while fully considering the output forecast error of renewable energy sources (RESs), the consumption preference of users and the status of the energy storage system. In this framework, we provide a unified appliance model to group various types of appliances, which are connected to the network, into a physical model with the same attributes, and a comprehensive real-time pricing mechanism between the RELN and power distribution company (PDC) is developed based on the generalized total load to guide the electricity consumption behavior of the end-users and to also balance the total residential load. On this basis, a mixed integer programming (MIP) model for dynamic energy management optimization of the RELN is optimized at each decision period and integrated into a model predictive control method to reduce the negative impacts of forecast errors of RESs, thereby realizing an optimal and automatic energy flow control for the RELN. Finally, the effectiveness of the proposed RELN energy flow management framework is verified using several case studies.

스마트 그리드에 있어서 저장 장치를 고려한 최적 에너지 소비 스케줄링 : 게임 이론적 접근

In this research, we consider a smart grid network of electricity with multiple consumers connected to a monopolistic provider. Each consumer can be informed the real time price changes through the smart meter and updates his consumption schedule to minimize the energy consumption expenditures by which the required power demand should be satisfied under the given real time pricing scheme. This real-time decision making problem has been recently studied through game-theoretic approach. The present paper contributes to the existing literature by incorporating storage appliance into the set of available household appliances which has somewhat distinctive functions compared to other types of appliances and would be regarded to play a significant role in energy consumption scheduling for the future smart grid. We propose a game-theoretic algorithm which could draw the optimal energy consumption scheduling for each household appliances including storage. Results on simulation data showed that the storage contributed to increase the efficiency of energy consumption pattern in the viewpoint of not only individual consumer but also whole system. 1)

Optimal residential appliance scheduling under dynamic pricing scheme via HEMDAS

With the emergence of smart grid which has advanced metering infrastructure (AMI) and microgrid running as a local energy provider for dwellings through distributed energy resources (DERs), dwellers have the opportunity to schedule their in-home energy usage by themselves to reduce energy expense. Household tasks along with DER operation are scheduled according to electricity real-time price (RTP) and natural gas fixed price. Various studies have shown that the lack of effective home automation systems and the lack of awareness among users to respond to time-varying prices are two chief obstacles in order to fully exploit the potential benefits of dynamic pricing schemes. This paper drives to handle these problems by proposing an automatic and optimal residential energy consumption scheduling technique which tries to achieve a favorable trade-off between minimizing the energy costs as well as the inconvenience for the operation of both electrical and thermal in a smart home environment. Simulation results show that the proposed scheduling method leads to significant reduction in energy costs for diverse load scenarios with the electricity demand from the grid. Therefore, the deployment of the proposed method is advantageous for both users and utilities.

Simulator for optimal scheduling of domestic appliances

Introduction of real-time pricing models for the price of electricity may lead to a reduction in both economic and environmental burden compared with existing models. Thanks to the timely response to price changes during the day the user can achieve a reduction in the payments for energy. However, recent studies show that lack of knowledge of users and their unwillingness to adapt their habits to changing energy prices is the biggest obstacle to successful implementation of the model. In our work, we propose to solve this problem by introducing automatic optimal energy consumption scheduling framework. This article presents the authors extension in the past created MILP model for the optimization of household appliances. The model is extended by rules of behavior that allows preserving logical links in the course of periodically triggered optimization (so called receding horizon). We also present and test the software simulator, in which the designed model is implemented. The application simulates the operation of Building Energy Manager, which based on user preferences, prediction electricity prices and other important parameters controls the operation of appliances in the house.

Multi-Objective Optimal Energy Consumption Scheduling in Smart Grids

A major source of inefficiency in power grids is the underutilization of generation capacity. This is mainly because load demand during peak hours is much larger than that during off-peak hours. Moreover, extra generation capacity is needed to maintain a security margin above peak load demand. As load demand keeps increasing and two-way communications are enabled by smart meters (SMs), demand response (DR) has been proposed as an alternative to installing new power plants in smart grids. DR makes use of real-time schemes to allow users to modify their load demand patterns according to their energy consumption costs. In particular, when load demand is high, energy consumption cost will be high and users may decide to postpone certain amount of their consumption needs. This strategy may effectively reduce the peak load demand and increases the off-peak demand, and hence could increase existing generation capacity utilization and reduce the need to install extra generation plants. In this paper, we consider a third-party managing the energy consumption of a group of users, and formulate the load scheduling problem as a constrained multi-objective optimization problem (CMOP). The optimization objectives are to minimize energy consumption cost and to maximize a certain utility, which can be conflicting and non-commensurable. We then develop two evolutionary algorithms (EAs) to obtain the Pareto-front solutions and the e-Pareto front solutions to the CMOP, respectively, which are validated by extensive simulation results.

Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments

Real-time electricity pricing models can potentially lead to economic and environmental advantages compared to the current common flat rates. In particular, they can provide end users with the opportunity to reduce their electricity expenditures by responding to pricing that varies with different times of the day. However, recent studies have revealed that the lack of knowledge among users about how to respond to time-varying prices as well as the lack of effective building automation systems are two major barriers for fully utilizing the potential benefits of real-time pricing tariffs. We tackle these problems by proposing an optimal and automatic residential energy consumption scheduling framework which attempts to achieve a desired trade-off between minimizing the electricity payment and minimizing the waiting time for the operation of each appliance in household in presence of a real-time pricing tariff combined with inclining block rates. Our design requires minimum effort from the users and is based on simple linear programming computations. Moreover, we argue that any residential load control strategy in real-time electricity pricing environments requires price prediction capabilities. This is particularly true if the utility companies provide price information only one or two hours ahead of time. By applying a simple and efficient weighted average price prediction filter to the actual hourly-based price values used by the Illinois Power Company from January 2007 to December 2009, we obtain the optimal choices of the coefficients for each day of the week to be used by the price predictor filter. Simulation results show that the combination of the proposed energy consumption scheduling design and the price predictor filter leads to significant reduction not only in users' payments but also in the resulting peak-to-average ratio in load demand for various load scenarios. Therefore, the deployment of the proposed optimal energy consumption scheduling schemes is beneficial for both end users and utility companies.