Thesis

Optimized Energy Management and Load Forecasting Systems for the Residential Sector

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Abstract

Electricity is the basic demand of consumers. With the passage of time, the demand for electricity is increasing day by day. Smart grid (SG) is evolved to satisfy the demand of consumers. To manage electricity load from peak hours to low peak hours, consumer needs to control their appliances by home energy management system (HEMS). HEMS schedule the appliances according to customers need. Energy management using demand-side management (DSM) techniques play an important role in SG domain. Smart meters (SM) and energy management controllers (EMC) are the important components of the SG. Intelligent energy optimization techniques play a vital role in the reduction of the electricity bill via scheduling home appliances. Through appliance’s scheduling, the consumer gets a feasible cost for consumed electricity. DSM provides the facility for consumers to schedule their appliances for the reduction of power price and rebate in peak loads. HEMS is allowed to remotely shut down their appliances in emergency conditions through direct load control programs. Meta-heuristic algorithms have been used for the optimization of the user energy consumption in an efficient way. Electricity load forecasting plays a vital role in improving the use of energy through customers to make decisions efficiently. The accuracy of load prediction is a challenging task because of randomness and noise disturbance. In this thesis, efficient algorithms are proposed to control the load in residential units. Our proposed schemes are used to minimize the user comfort delay time. Customers waiting time is inversely proportional to the total cost and peak to average ratio (PAR). The aim of the current research is to manage the power of the residential units in an optimized way and predict the exact load. Simulation results show the minimum user waiting time, however, the total cost is compromised due to the high demand of the load and predict the exact load for users. In the end, our proposed schemes show better result through simulation results. In this thesis, we proposed new schemes which are used to lower the electricity price, PAR and user discomfort in electricity consumption side. The proposed schemes performed better than existing benchmark schemes. The proposed schemes used real-time price (RTP) signal for calculating the electricity cost and PAR. Simulation results also show that the proposed algorithms have met the objective of DSM. For prediction, the proposed scheme is performed better than benchmark schemes and predict the exact electricity load.

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It is important for building owners and operators to manage the electrical energy consumption of their buildings. As electrical energy is the major form of energy consumed in a commercial building, the ability to forecast electrical energy consumption in a building will bring great benefits to the building owners and operators. This paper provides a review of the building electrical energy consumption forecasting methods which include the conventional and artificial intelligence (AI) methods. The significant goal of this study is to review, recognize, and analyse the performance of both methods for forecasting of electrical energy consumption. Compared to using a single method of forecasting, the hybrid of two forecasting methods can possibly be applied for more precise results. Regarding this potential, the swarm intelligence (SI) method has been reviewed to be hybridized with AI. Published literature presented in this paper shows that, the hybrid of SVM and SI methods has indeed presented superior performance for forecasting building electrical energy consumption.
Article
Short-term load forecasting (STLF) models are very important for electric industry in the trade of energy. These models have many applications in the day-today operations of electric utility(ies) like energy generation planning, load switching, energy purchasing, infrastructure maintenance and contract evaluation. A large variety of STLF models have been developed which trade-off between forecast accuracy and convergence rate. This paper presents an accurate and fast converging STLF (AFC-STLF) model for industrial applications in a smart grid. In order to improve the forecast accuracy, modifications are devised in two popular techniques: (1) mutual information (MI) based feature selection, and (2) enhanced differential evolution (EDE) algorithm based error minimization. On the other hand, the convergence rate of the overall forecast strategy is enhanced by devising modifications in the heuristic algorithm and in the training process of the artificial neural network (ANN). Simulation results show that accuracy of the newly proposed forecast model is 99.5% with moderate execution time, i.e., we have decreased the average execution of the existing Bi-level forecast strategy by 52.38%.
Article
This paper investigates different procedures for economic power dispatching in an islanded microgrid with multiple generators. Optimal scheduling strategies are introduced considering the forecasts for both consumption (powering of greenhouse equipment) and production (photovoltaic systems, geothermal and biomass generators). Several dispatching scenarios were generated using constrained optimization procedures. The paper introduces an experimental system equipped with smart metering instruments intended as a setup for validating the results obtained.
Article
With constructions of demonstrative microgrids, the realistic optimal economic dispatch and energy management system are required eagerly. However, most current works usually give some simplifications on the modeling of microgrids. This paper presents an optimal day-ahead scheduling model for a microgrid system with photovoltaic cells, wind turbine units, diesel generators and battery storage systems. The power flow constraints are introduced into the scheduling model in order to show some necessary properties in the low voltage distribution network of microgrids. Besides a hybrid harmony search algorithm with differential evolution (HSDE) approach to the optimization problem is proposed. Some improvements such as the dynamic F and CR, the improved mutation, the additional competition and the discrete difference operation have been integrated into the proposed algorithm in order to obtain the competitive results efficiently. The numerical results for several test microgrids adopting the IEEE 9-bus, IEEE 39-bus and IEEE 57-bus systems to represent their transmission networks are employed to show the effectiveness and validity of the proposed model and algorithm. Not only the normal operation mode but also some typical fault modes are used to verify the proposed approach and the simulations show the competitiveness of the HSDE algorithm.
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Flood forecasting in natural rivers is a complicated procedure because of uncertainties involved in the behaviour of the flood wave movement. This leads to complex problems in hydrological modelling which have been widely solved by soft computing techniques. In real time flood forecasting, data generation is continuous and hence there is a need to update the developed mapping equation frequently which increases the computational burden. In short term flood forecasting where the accuracy of flood peak value and time to peak are critical, frequent model updating is unavoidable. In this paper, we studied a new technique: Online Sequential Extreme Learning Machine (OS-ELM) which is capable of updating the model equation based on new data entry without much increase in computational cost. The OS-ELM was explored for use in flood forecasting on the Neckar River, Germany. The reach was characterized by significant lateral flow that affected the flood wave formation. Hourly data from 1999-2000 at the upstream section of Rottweil were used to forecast flooding at the Oberndorf downstream site with a lead time of one to six hours. Model performance was assessed by using three evaluation measures: the coefficient of determination (R2), the Nash-Sutcliffe efficiency coefficient (NS) and the root mean squared error (RMSE). The performance of the OS-ELM was comparable to those of other widely used Artificial Intelligence (AI) techniques like Support Vector Machines (SVM), Artificial Neural Networks (ANN) and Genetic Programming (GP). The frequent updating of the model in OS-ELM gave a closer reproduction of flood events and peak values with minimum error compared to SVM, ANN and GP.
Conference Paper
Today, energy is the most valuable resource, new methods and techniques are being discovered to fulfill the demand of energy. However, energy demand growth causes a serious energy crisis, especially when demand is comparatively high and creates the peak load. This problem can be handled by integrating Demand Side Management (DSM) with traditional Smart Grid (SG) through two way communication between utility and customers. The main objective of DSM is peak load reduction where SG targets cost minimization and user comfort maximization. In this study, our emphasis is on cost minimization and load management by shifting the load from peak hours toward the off peak hours. In this underlying study, we adapt hybridization of two optimization approaches, Bacterial Foraging (BFA) and Genetic Algorithm (GA). Simulation results verify that the adapted approach reduces the total cost and peak average ratio by shifting the load on off peak hours with very little difference between minimum and maximum 95% confidence interval.
Article
This paper proposes a novel framework suitable for bilevel optimization in a system of commercial buildings integrated to smart distribution grid. The proposed optimization framework consists of comprehensive mathematical models of commercial buildings and underlying distribution grid, their operational constraints, and a bilevel solution approach which is based on the information exchange between the two levels. The proposed framework benefits both entities involved in the building-to-grid (B2G) system, i.e., the operations of the buildings and the distribution grid. The framework achieves two distinct objectives: increased load penetration by maximizing the distribution system load factor and reduced energy cost for the buildings. This study also proposes a novel B2G index, which is based on building’s energy cost and nodal load factor, and represents a metric of combined optimal operations of the commercial buildings and distribution grid. The usefulness of the proposed framework is demonstrated in a B2G system that consists of several commercial buildings connected to a 33-node distribution test feeder, where the building parameters are obtained from actual measurements at an office building at Michigan Technological University.
Article
This paper studies two energy management problems under uncertainties for a grid-connected microgrid. The problems are motivated by practical microgrid applications such as peak power shaving and frequency regulation. These applications require constraints on the microgrid energy output which is uncertain due to the integration with renewable resources and random loads. Both problems are formulated as chance constrained programming problems to systematically incorporate uncertainties. We also show that the resulting chance constrained programming problems can both be solved using linear programming. The proposed formulation and solution are verified by two case studies originated from real world applications.
Article
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.
Article
This paper describes a load profile prediction algorithm that has recently been developed for use in the optimal operation of cool/heat storage systems. It is highly desirable to devise an on-line prediction technique so that the difference between a predicted load and an actual load is as small as possible. To accomplish this, an autoregressive integrated moving average (ARIMA) model is assumed. The modeling is first done for the past load data. Next, the model predicts load profiles for the next day. The load profiles are updated every hour on the basis of the newly obtained load data. Performance of the load profile prediction algorithm, was assessed by recording the cooling load (1987) at an Osaka site. Results show generally good agreement between actual and predicted loads.
Article
Smart scheduling of energy consuming devices in the domestic sector should factor in clean energy generation potential, electricity tariffs, and occupants' behaviour (i.e., interactions with their appliances). The paper presents an Artificial Neural Network/Genetic Algorithm (ANN-GA) smart appliance scheduling approach for optimised energy management in the domestic sector. The proposed approach reduces energy demand in "peak" periods, maximises use of renewable sources (PV and wind turbine), while reducing reliance on grid energy. Comprehensive parameter optimisation has been carried out for both ANN and GA to find the best combinations, resulting in optimum weekly schedules. The proposed artificial intelligence techniques involve a holistic understanding of (near) real-time energy demand and supply within a domestic context to deliver optimised energy usage with minimum computational needs. The solution is stress-tested and demonstrated in a four bedroom house with grid energy usage reduction by 10%, 25%, and 40%, respectively.
Article
The current energy requirements of buildings comprise a large percentage of the total energy consumed around the world. The demand of energy, as well as the construction materials used in buildings, are becoming increasingly problematic for the earth's sustainable future, and thus have led to alarming concern. The energy efficiency of buildings can be improved, and in order to do so, their operational energy usage should be estimated early in the design phase, so that buildings are as sustainable as possible. An early energy estimate can greatly help architects and engineers create sustainable structures. This study proposes a novel method to estimate building energy consumption based on the ELM (Extreme Learning Machine) method. This method is applied to building material thicknesses and their thermal insulation capability (K-value). For this purpose up to 180 simulations are carried out for different material thicknesses and insulation properties, using the EnergyPlus software application. The estimation and prediction obtained by the ELM model are compared with GP (genetic programming) and ANNs (artificial neural network) models for accuracy. The simulation results indicate that an improvement in predictive accuracy is achievable with the ELM approach in comparison with GP and ANN.
Article
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.
Article
With the development of home area network, residents have the opportunity to schedule their power usage in the home by themselves aiming at reducing electricity expenses. Moreover, as renewable energy sources are deployed in home, a home energy management system needs to consider both energy consumption and generation simultaneously to minimize the energy cost. In this paper, a smart home energy management model has been presented in which electrical and thermal appliances are jointly scheduled. The proposed method aims at minimizing the electricity cost of a residential customer by scheduling various type of appliances considering the residents consumption behavior, seasonal probability, social random factor, discomfort index and appliances starting probability functions. In this model, the home central controller receives the electricity price information, environmental factors data as well as the resident desired options in order to optimally schedule appliances including electrical and thermal. The scheduling approach is tested on a typical home including variety of home appliances, a small wind turbine, photovoltaic panel, combined heat and power unit, boiler and electrical and thermal storages over a 24-h period. The results show that the scheduling of different appliances can be reached simultaneously by using the proposed formulation. Moreover, simulation results evidenced that the proposed home energy management model exhibits a lower cost and, therefore, is more economical.
Article
In this paper, we focus on the problems of load scheduling and power trading in systems with high penetration of renewable energy resources (RERs). We adopt approximate dynamic programming to schedule the operation of different types of appliances including must-run and controllable appliances. We assume that users can sell their excess power generation to other users or to the utility company. Since it is more profitable for users to trade energy with other users locally, users with excess generation compete with each other to sell their respective extra power to their neighbors. A game theoretic approach is adopted to model the interaction between users with excess generation. In our system model, each user aims to obtain a larger share of the market and to maximize its revenue by appropriately selecting its offered price and generation. In addition to yielding a higher revenue, consuming the excess generation locally reduces the reverse power flow, which impacts the stability of the system. Simulation results show that our proposed algorithm reduces the energy expenses of the users. The proposed algorithm also facilitates the utilization of RERs by encouraging users to consume excess generation locally rather than injecting it back into the power grid.