Multi-objective optimization of household appliance scheduling problem considering consumer preference and peak load reduction

Abstract:This paper addresses the load scheduling problem in residential sector while considering preferences of consumers and reduction of peak load. This study proposes an optimization model using multi-objective mixed integer linear programming considering a time-of-use (ToU) electricity tariff. Furthermore, this study considers the coordinated peak load reduction in a multiple-household environment. The proposed model aims to minimize three objectives: the electricity cost, the scheduling inconvenience and the peak load. Considering three objectives could enable consumers and utility companies to control their priority in minimizing one over the others. Three multi-objective optimization approaches are applied to solve the proposed model: normalized weighted-sum approach, preemptive optimization and compromise optimization. Numerical experiments show that the proposed solutions lead to significant savings in electricity costs, eliminate consumer inconvenience, while reducing the system peak loading. Furthermore, the results show outstanding performance when compared against three schedules from the literature and the consumer’s preferred schedule. Moreover, the coordinated schedules for the multiple-household problem lead to a significant reduction and levelling of the aggregated peak load

Development of a power monitoring and control system to provide demand side management of electric vehicle charging activity.

Due to the recent inflow of Electric Vehicles (EVs) to the automobile market, new concerns have risen with respect to the additional electrical load and the resultant effects on an overloaded electric grid. Either for convenience purposes or possibly necessity due to limited electric range on EVs, some EV owners may desire to charge their EV while at work in addition to charging at home. These forward-thinking daytime charging providers are typically Commercial and Industrial (C&I) electric ratepayers, or other large electric consumers which constitute the majority of businesses, shopping centers, academic campuses and manufacturing facilities. Increased electricity consumption due to EV charging activity results in higher electricity costs due to differences in the billing structures between residential and C&I electric ratepayers. Therefore, it is beneficial to the EVSE charging provider to minimize charging activity around peak demand periods which would result in lower electrical costs overall. A solution is developed that can provide this control without creating a nuisance to electric vehicle owners since EV charging demand is somewhat inelastic due to range anxiety. The primary objective of the research detailed in this dissertation is to develop a novel demand side management system for monitoring the peak demand of commercial time-of-day electric ratepayers that cost effectively predicts and controls electric vehicle charging during peak demand periods. This objective is achieved, therefore confirming the hypothesis that such a system can provide cost and demand benefits to forward-thinking commercial electric ratepayers that provide daytime charging capabilities. This work proposes and evaluates a novel Power Monitoring and Control System (PMCS) that can be implemented at C&I EV charging locations to minimize or eliminate the negative impacts of charging electric vehicles at the workplace in C&I environments. Operation of the PMCS begins by forecasting electrical demand in advance of every 15 minute demand interval throughout the day. The forecast is generated using an artificial neural network and a number of input data streams. Electrical demand has been shown to correlate well with weather data such as temperature and dew point. Therefore, using those measurements along with a date and time stamp, and historical electrical demand measurements, a highly accurate forecast for the following 15-minute demand interval was achieved. From that forecast, the number of EV charging stations that may be active, without the chance of creating new electrical demand peaks, is calculated. Finally, the forecast is then used to properly schedule EV charging activity so that electrical demand peaks can be avoided but charging activity is maximized. The avoidance of charging activity at or near peaks in electrical demand results in lower total electric costs associated with the charging process. The final design was implemented in an EV charging testbed at the University of Louisville and data was collected to verify the operation and performance of the PMCS. With a properly designed scheduling and prioritization control algorithm, increases in electrical demand and associated costs are limited to the error in the forecasting algorithm used for predicting electrical demand levels. The final design of the forecasting algorithm results in a mean absolute percent error of 0.02% to 0.08% in the electrical demand forecast. This corresponds to approximately 3 to 10 kVA of error in electrical demand. Taking this error into account, total cost of charging several EVs is reduced by nearly 90%. Furthermore, for scenarios where there are several more electric vehicles requiring charge than there are charging stations available, several scheduling algorithms are presented in an attempt to minimize the total processing time required for completing all charging transactions

Model-based development and design of microgrid power systems

The motivation of this contribution is the study of the Microgrid (MG) power systems, specifically Solar-Battery-Diesel systems, which are used to support unreliable grids and provide a continuous electricity supply to the areas which have limited or even no access to the grid. The main research focus in the scientific community lies on the development of general energy management strategies (EMSs) for optimal power routing on the one hand, and providing an optimized layout-design of the MGs on the other hand. However, none of these two issues can be adequately handled in isolation from one another because they have a direct impact on each other. This work aims at tackling Model-Based Development and Design of Microgrid Power Systems by addressing the challenges of the EMSs and the layout-design based on the system model and operational requirements. For this purpose, several EMSs for scheduling generation side in the MG are developed in accordance with the operational constraints. Besides, a forecast-driven power planning approach is developed for MGs that incorporate smart shiftable loads. Furthermore, this work proposes an integrated layout-design method for optimizing the size of the microgrid considering the applied EMS. Finally, to highlight the usefulness of the developed EMSs and design approach, different examples inspired from a real case-study are presented.Diese Arbeit befasst sich mit dem Thema Microgrid (MG)-Energieversorgungssysteme, hauptsächlich Solar-Batterie-Diesel-Systeme, die vor allem zur Unterstützung der unzuverlässige Netze eingesetzt werden oder als eine zuverlässige Stromversorgung für Gebiete, die nur begrenzten oder sogar keinen Zugang zu elektrischem Strom haben. Der aktuelle Forschungsschwerpunkt in der Wissenschaft liegt einerseits in der Entwicklung allgemeiner Energiemanagementstrategien (EMS) für eine optimale Energieführung und andererseits in einem optimierten Design der MGs. Keines dieser beiden Probleme kann jedoch isoliert betrachtet werden, da sie sich direkt aufeinander auswirken. Diese Arbeit zielt darauf ab, die Modellbasierte Entwicklung und Auslegung von Microgrid-Energieversorgungssystemen anzugehen, indem die Herausforderungen der EMS und des Layout-Designs basierend auf dem Systemmodell und den betrieblichen Anforderungen adressiert werden. Zu diesem Zweck werden mehrere EMS zur Planung der Erzeugungsseite in dem MG in Übereinstimmung mit den Betriebsbeschränkungen entwickelt. Außerdem wird für die Microgrids, die intelligent verschiebbare Lasten enthalten, eine prognosebasierte Betriebsstrategie entwickelt. Diese Arbeit schlägt auch eine integrierte Layout-Design-Methode vor, um die Auslegung des Microgrids unter Berücksichtigung des angewandten EMS zu optimieren. Um die Wirksamkeit der entwickelten EMS und des Entwurfsansatzes hervorzuheben, werden schließlich verschiedene Beispiele vorgestellt, die von einer realen Fallstudie inspiriert sind

Intelligent Decision Support System for Energy Management in Demand Response Programs and Residential and Industrial Sectors of the Smart Grid

This PhD thesis addresses the complexity of the energy efficiency control problem in residential and industrial customers of Smart electrical Grid, and examines the main factors that affect energy demand, and proposes an intelligent decision support system for applications of demand response. A multi criteria decision making algorithm is combined with a combinatorial optimization technique to assist energy managers to decide whether to participate in demand response programs or obtain energy from distributed energy resources

Microgrid Energy Management with Flexibility Constraints: A Data-Driven Solution Method

Microgrid energy management is a challenging and important problem in modern power systems. Several deterministic and stochastic models have been proposed in the literature for the microgrid energy management problem. However, more accurate models are required to enhance flexibility of the microgrids when accounting for renewable energy and load uncertainties. This thesis proposes key contributions to solve the energy management problem for smart building (or small-scale microgrid). In Chapter 3, a deterministic energy management model is presented taking into account system flexibility requirements. Energy storage systems are deployed to enhance the grid flexibility and ramping capability. The objective function of the formulated optimization is to minimize the operation cost. Combined heat and power (CHP) units, which interconnect heat and electricity, are modeled. Thus, electricity and thermal generation and load constraints are formulated. To account for uncertainties of load and renewable energy resources (e.g., solar generation), a stochastic energy management model is proposed in Chapter 4. A data-driven chance-constrained optimization is based method is formulated. The proposed model is nonparametric that imposes no assumption on probability distribution functions (PDFs) of the random variables (i.e., load and renewable generation). Adaptive kernel density estimation is deployed to estimate a nonparametric PDF for each random variable. Confidence levels (risk levels) of the chance constraints are modified according to estimation errors. Several cases are simulated to analyze the deterministic and stochastic optimization models. The simulation results show that the proposed data-driven chance-constrained optimization with the flexibility constraints enhance reliability, resiliency, and economics of the microgrid energy systems. Note that these flexibility constraints avoid propagating solar and load fluctuations to the distribution feeder. That is smart building (microgrid) is capable of capturing fluctuations locally

Demand-Response in Smart Buildings

This book represents the Special Issue of Energies, entitled “Demand-Response in Smart Buildings”, that was published in the section “Energy and Buildings”. This Special Issue is a collection of original scientific contributions and review papers that deal with smart buildings and communities. Demand response (DR) offers the capability to apply changes in the energy usage of consumers—from their normal consumption patterns—in response to changes in energy pricing over time. This leads to a lower energy demand during peak hours or during periods when an electricity grid’s reliability is put at risk. Therefore, demand response is a reduction in demand designed to reduce peak load or avoid system emergencies. Hence, demand response can be more cost-effective than adding generation capabilities to meet the peak and/or occasional demand spikes. The underlying objective of DR is to actively engage customers in modifying their consumption in response to pricing signals. Demand response is expected to increase energy market efficiency and the security of supply, which will ultimately benefit customers by way of options for managing their electricity costs leading to reduced environmental impact

SALSA: A Formal Hierarchical Optimization Framework for Smart Grid

The smart grid, by the integration of advanced control and optimization technologies, provides the traditional grid with an indisputable opportunity to deliver and utilize the electricity more efficiently. Building smart grid applications is a challenging task, which requires a formal modeling, integration, and validation framework for various smart grid domains. The design flow of such applications must adapt to the grid requirements and ensure the security of supply and demand. This dissertation, by proposing a formal framework for customers and operations domains in the smart grid, aims at delivering a smooth way for: i) formalizing their interactions and functionalities, ii) upgrading their components independently, and iii) evaluating their performance quantitatively and qualitatively.The framework follows an event-driven demand response program taking no historical data and forecasting service into account. A scalable neighborhood of prosumers (inside the customers domain), which are equipped with smart appliances, photovoltaics, and battery energy storage systems, are considered. They individually schedule their appliances and sell/purchase their surplus/demand to/from the grid with the purposes of maximizing their comfort and profit at each instant of time. To orchestrate such trade relations, a bilateral multi-issue negotiation approach between a virtual power plant (on behalf of prosumers) and an aggregator (inside the operations domain) in a non-cooperative environment is employed. The aggregator, with the objectives of maximizing its profit and minimizing the grid purchase, intends to match prosumers' supply with demand. As a result, this framework particularly addresses the challenges of: i) scalable and hierarchical load demand scheduling, and ii) the match between the large penetration of renewable energy sources being produced and consumed. It is comprised of two generic multi-objective mixed integer nonlinear programming models for prosumers and the aggregator. These models support different scheduling mechanisms and electricity consumption threshold policies.The effectiveness of the framework is evaluated through various case studies based on economic and environmental assessment metrics. An interactive web service for the framework has also been developed and demonstrated