Assessment of novel distributed control techniques to address network constraints with demand side management

The development of sustainable generation, a reliable electricity supply and affordable tariffs are the primary requirements to address the uncertainties in different future energy scenarios. Due to the predicted increase in Distributed Generation (DG) and load profile changes in future scenarios, there are significant operational and planning challenges facing netwrok operators. These changes in the power system distribution network require a new Active Network Management (ANM) control system to manage distribution constraint issues such as thermal rating, voltage, and fault levels. The future smart grid focuses on harnessing the control potential from demand side via bidirectional power flow, transparent information communication, and contractual customer participation. Demand Side Management (DSM) is considered as one of the effective solutions to defer network capacity reinforcement, increase energy efficiency, facilitate renewable access, and implement low carbon energy strategy. From the Distribution Network Operator's (DNO) perspective, the control opportunity from Demand Response (DR) and Decentralized Energy Resource (DER) contributes on capacity investment reduction, energy efficiency, and enable low carbon technologies. This thesis develops a new decentralized control system for dealing effectively with the constraint issues in the Medium Voltage (MV) distribution network. In the decentralized control system, two novel control approaches are proposed to autonomously relieve the network thermal constraint via DNO's direct control of the real power in network components during the operation period. The first approach, Demand Response for Power Flow Management (DR-PFM), implements the DSM peak clipping control of Active Demand (AD), whilst the second approach, Hybrid Control for Power Flow Management (HC-PFM), implements the hybrid control of both AD and DER. The novelty of these two new control algorithms consists in the application of a Constraint Satisfaction Problem (CSP) based programming model on decision making of the real power curtailment to relieve the network thermal overload. In the Constraint Programming (CP) model, three constraints are identified: a preference constraint, and a network constraint. The control approaches effectively solve the above constraint problem in the CSP model within 5 seconds' time response. The control performance is influenced by the pre-determined variable, domain and constraint settings. These novel control approaches take advantages on flexible control, fast response and demand participation enabling in the future smart grid.The development of sustainable generation, a reliable electricity supply and affordable tariffs are the primary requirements to address the uncertainties in different future energy scenarios. Due to the predicted increase in Distributed Generation (DG) and load profile changes in future scenarios, there are significant operational and planning challenges facing netwrok operators. These changes in the power system distribution network require a new Active Network Management (ANM) control system to manage distribution constraint issues such as thermal rating, voltage, and fault levels. The future smart grid focuses on harnessing the control potential from demand side via bidirectional power flow, transparent information communication, and contractual customer participation. Demand Side Management (DSM) is considered as one of the effective solutions to defer network capacity reinforcement, increase energy efficiency, facilitate renewable access, and implement low carbon energy strategy. From the Distribution Network Operator's (DNO) perspective, the control opportunity from Demand Response (DR) and Decentralized Energy Resource (DER) contributes on capacity investment reduction, energy efficiency, and enable low carbon technologies. This thesis develops a new decentralized control system for dealing effectively with the constraint issues in the Medium Voltage (MV) distribution network. In the decentralized control system, two novel control approaches are proposed to autonomously relieve the network thermal constraint via DNO's direct control of the real power in network components during the operation period. The first approach, Demand Response for Power Flow Management (DR-PFM), implements the DSM peak clipping control of Active Demand (AD), whilst the second approach, Hybrid Control for Power Flow Management (HC-PFM), implements the hybrid control of both AD and DER. The novelty of these two new control algorithms consists in the application of a Constraint Satisfaction Problem (CSP) based programming model on decision making of the real power curtailment to relieve the network thermal overload. In the Constraint Programming (CP) model, three constraints are identified: a preference constraint, and a network constraint. The control approaches effectively solve the above constraint problem in the CSP model within 5 seconds' time response. The control performance is influenced by the pre-determined variable, domain and constraint settings. These novel control approaches take advantages on flexible control, fast response and demand participation enabling in the future smart grid

Deep Neural Networks for Multivariate Prediction of Photovoltaic Power Time Series

The large-scale penetration of renewable energy sources is forcing the transition towards the future electricity networks modeled on the smart grid paradigm, where energy clusters call for new methodologies for the dynamic energy management of distributed energy resources and foster to form partnerships and overcome integration barriers. The prediction of energy production of renewable energy sources, in particular photovoltaic plants that suffer from being highly intermittent, is a fundamental tool in the modern management of electrical grids shifting from reactive to proactive, with also the help of advanced monitoring systems, data analytics and advanced demand side management programs. The gradual move towards a smart grid environment impacts not only the operating control/management of the grid, but also the electricity market. The focus of this article is on advanced methods for predicting photovoltaic energy output that prove, through their accuracy and robustness, to be useful tools for an efficient system management, even at prosumer's level and for improving the resilience of smart grids. Four different deep neural models for the multivariate prediction of energy time series are proposed; all of them are based on the Long Short-Term Memory network, which is a type of recurrent neural network able to deal with long-term dependencies. Additionally, two of these models also use Convolutional Neural Networks to obtain higher levels of abstraction, since they allow to combine and filter different time series considering all the available information. The proposed models are applied to real-world energy problems to assess their performance and they are compared with respect to the classic univariate approach that is used as a reference benchmark. The significance of this work is to show that, once trained, the proposed deep neural networks ensure their applicability in real online scenarios characterized by high variability of data, without requiring retraining and end-user's tricks

Smart sockets: An enabling system for domestic consumer based demand - response electrical energy management programs

Electrical energy demand in developing countries is expected to rise significantly by the year 2020. To achieve electrical grid operational reliability the electrical grid operators need to keep a balance between their generation and consumption curve. The increase in demand requires expansion in the current generation capacity. This expansion can be achieved by bringing more green and renewable electrical energy generation resources into the electrical grid system. Keeping the electrical generation and distribution system in stable form at all times is a very complex task. Electricity utilities need to keep a balance between generation and consumption curves in order to achieve reliable grid operations. Grid operators have used many techniques to achieve reliability in grid operations. Demand-side management and demand response strategies are examples of a few of those techniques. “Smart grid” is the overall solution of most of these problems. However, smart grid has its own pros and cons. On the other hand, electrical energy consumers at present are not actively participating in the operations of electrical grids. This lack of participation is both in developed and developing countries. Research has shown that benefits were achieved when electrical energy consumers participated in the electrical grid operations. Grid operators in the past have used different demand response strategies to minimize this gap of interaction between the electrical energy consumers and electricity utilities. Demand response programs or strategies used at present are predominantly for commercial or large electrical energy consumers. There are very few of the demand response strategies deployed for domestic electrical energy consumers. All the present demand response strategies are utility-based strategies, that is electricity utilities have full control over a domestic consumer’s electrical energy consumption. When required utilities can switch the appliance ON or OFF in a domestic consumer’s household. This utility driven strategy can result in consumer’s dissatisfaction. Utilities need to offer demand response programs for the domestic electrical energy consumers. However offering consumer based demand response programs require the active participation from the consumer’s side. Electricity utilities must provide their consumers with the information about their electrical energy consumption in realtime. This requires equipment that can monitor electrical energy consumption in realtime and send it back to the utility and the consumer. Smart meters have been used to serve the purpose, however they have their problems. The domestic consumer needs to be onsite (at his home) in order to benefit from the information displayed on a smart meter. Since domestic consumers are not at home at all times the information displayed on the smart meter is not useful. This makes the use of smart meters inefficient. This dissertation proposes a system based on the concept of smart power sockets which can be used at a domestic electrical energy consumer’s household in order to monitor their electrical energy in real-time and alert them about their electrical energy consumption when required. These smart power sockets also enable the consumers to control their appliance remotely (to switch them ON or OFF when required). The prototype system has been developed and tested. The results suggest that domestic electrical energy consumers reacted to the information provided to them about their electrical energy information. Based on the information provided the domestic electrical energy consumer was able to reduce his/her electrical energy consumption. In some cases it is also noticed that the domestic electrical energy consumer shifted some of his/her load from peak time of electrical energy demand to the off peak time. Based on these results it is recommended that electricity utilities must consider offering demand response programs that are mainly driven by the domestic electrical energy consumers themselves

Mathematical optimization techniques for demand management in smart grids

The electricity supply industry has been facing significant challenges in terms of meeting the projected demand for energy, environmental issues, security, reliability and integration of renewable energy. Currently, most of the power grids are based on many decades old vertical hierarchical infrastructures where the electric power flows in one direction from the power generators to the consumer side and the grid monitoring information is handled only at the operation side. It is generally believed that a fundamental evolution in electric power generation and supply system is required to make the grids more reliable, secure and efficient. This is generally recognised as the development of smart grids. Demand management is the key to the operational efficiency and reliability of smart grids. Facilitated by the two-way information flow and various optimization mechanisms, operators benefit from real time dynamic load monitoring and control while consumers benefit from optimised use of energy. In this thesis, various mathematical optimization techniques and game theoretic frameworks have been proposed for demand management in order to achieve efficient home energy consumption scheduling and optimal electric vehicle (EV) charging. A consumption scheduling technique is proposed to minimise the peak consumption load. The proposed technique is able to schedule the optimal operation time for appliances according to the power consumption patterns of the individual appliances. A game theoretic consumption optimization framework is proposed to manage the scheduling of appliances of multiple residential consumers in a decentralised manner, with the aim of achieving minimum cost of energy for consumers. The optimization incorporates integration of locally generated and stored renewable energy in order to minimise dependency on conventional energy. In addition to the appliance scheduling, a mean field game theoretic optimization framework is proposed for electric vehicles to manage their charging. In particular, the optimization considers a charging station where a large number of EVs are charged simultaneously during a flexible period of time. The proposed technique provides the EVs an optimal charging strategy in order to minimise the cost of charging. The performances of all these new proposed techniques have been demonstrated using Matlab based simulation studies

A Human Side Of The Smart Grid: Behavior-Based Energy Efficiency From Renters Using Real-Time Feedback And Competitive Performance-Based Incentives

Our energy system is rapidly transforming, partially due to advances in internet and communications technologies that leverage an unprecedented amount of data. Industry proponents of the so-called “smart grid” suggest these technologies facilitate deeper engagement with end-users of energy (utility customers) that can in turn drive behavior-based changes and accelerate a renewable energy transition. While there has been progress in understanding how these technologies change consumer behavior using, for example, real-time feedback, it’s unclear how specific segments (e.g., renters) respond to these interventions; it’s also unclear why feedback is, or is not, producing changes in energy consumption. The literature suggests that behavioral strategies (e.g. information feedback, competitions, incentives) coupled with technology may present a way for utilities and efficiency programs to create savings—expanding opportunities for those often underserved by traditional approaches, such as renters—yet this coupling is not well understood, neither broadly (for all end users) nor specifically (for renters). This dissertation builds upon that literature and explores a human side of the smart grid, using a field experiment in renter households to test the interacting effects of real-time energy feedback and a novel form of financial incentive, referred to here as a competitive performance-based incentive. The experiment had two phases: phase one tested the feedback against a control group; phase two tested feedback, the incentive, and a combined treatment, against a control group. Results of these interventions were measured with pre- and post-treatment surveys as well as observed electricity consumption data from each household’s smart meter. The results of this experiment are described in three papers. Paper one examines the interventions’ individual and combined effectiveness at motivating renters to reduce or shift timing of electricity consumption. Feedback alone produced a significant savings effect in phase one. In phase two, the effect of the feedback wore off; the incentive alone had no significant effect; and the group that received feedback and the incentive experienced a doubling of savings relative to the effect of feedback alone, as observed in phase one. Paper two uses pre- and post-intervention survey data to examine how individual perceptions of energy change as a result of the interventions. Perception of large energy-using appliances changed the most in households that received feedback, suggesting that better information may lead to more effective behavior changes. Paper three leverages the results of the first two components to evaluate the policy implications and impacts on demand side management for utilities, efficiency programs, and the potential for behavior-based energy efficiency programs. Advocates of the smart grid must recognize the technology alone cannot produce savings without better engagement of end-users. Utility rate designers must carefully consider how time-based rates alone may over-burden those without the enabling technology to understand the impact of their energy choices

Home energy management system : a home energy management system under different electricity pricing mechanisms

Masteroppgave i fornybar energi ENE 500 Universitetet i Agder 2014Peak demand is a severe problem in the electricity grid and it was solved by supply side management in the past. But nowadays the demand side management sources have drawn attention due to the economic and environmental constraints. Demand side management in the domestic sector can play an important role in reducing the peak demand on the power system network. It can help in reducing stress and overloading on the transmission and distribution lines. In many countries there are various demand response programs implemented for industrial and commercial loads. In these programs load control is primarily achieved by various types of pricing mechanisms. There are very few demand response programs in use for energy management in residential sector. Direct curtailment of the loads is the most popular method used to reduce the peak demand in the domestic sector. But by direct load control, customer comfort may be compromised. In contrast peak load reduction through load shifting can benefit both consumers and utilities. In order to analyze demand response in the domestic sector, it is important to understand physical based power intensive load models with an emphasis on water heater units, air conditioner units, clothes dryers and electric vehicles. In this work, these load models are developed considering thermodynamic principles of buildings as well as their built in technical parameters. With the development of smart grid systems specially in the distribution network and possibility of load modeling, there is a requirement of a domestic intelligent energy management algorithm. In this work, power intensive non-critical loads are managed through developed energy management system algorithm and these loads are water heater, air conditioning unit, clothes dryer and electric vehicle. With the introduction of electric vehicles, demand responses can be performed within home for avoiding any overloading problems in the distribution network as well as on power generation. Additionally, the electricity bill saving which can be gained through proposed energy management system is analyzed by considering different electricity pricing mechanisms. The highlight of the presented energy management system algorithm for home energy management is its capability to control the non-critical loads below specified peak demand limits by considering consumer behavior and priorities, giving consumers more flexibility in their operational time. Moreover, the results show that the electricity saving which can be gained through the proposed energy management system lies in a noticeably high range. It is expected that the research findings of this work can be beneficial to utilities in providing information of limits and scope of domestic demand responses. And also it is anticipated that the cost analysis carried out can be used to motivate the consumers towards demand response through the developed energy management system. Key words: Domestic demand response, Home energy management system (EMS), demand limits, non-critical loads, load priority, Time of Use pricing, Real Time Pricin

Optimal Energy Management of Distribution Systems and Industrial Energy Hubs in Smart Grids

Electric power distribution systems are gradually adopting new advancements in communication, control, measurement, and metering technologies to help realize the evolving concept of Smart Grids. Future distribution systems will facilitate increased and active participation of customers in Demand Side Management activities, with customer load profiles being primarily governed by real-time information such as energy price, emission, and incentive signals from utilities. In such an environment, new mathematical modeling approaches would allow Local Distribution Companies (LDCs) and customers the optimal operation of distribution systems and customer's loads, considering various relevant objectives and constraints. This thesis presents a mathematical model for optimal and real-time operation of distribution systems. Thus, a three-phase Distribution Optimal Power Flow (DOPF) model is proposed, which incorporates comprehensive and realistic models of relevant distribution system components. A novel optimization objective, which minimizes the energy purchased from the external grid while limiting the number of switching operations of control equipment, is considered. A heuristic method is proposed to solve the DOPF model, which is based on a quadratic penalty approach to reduce the computational burden so as to make the solution process suitable for real-time applications. A Genetic Algorithm based solution method is also implemented to compare and benchmark the performance of the proposed heuristic solution method. The results of applying the DOPF model and the solution methods to two distribution systems, i.e., the IEEE 13-node test feeder and a Hydro One distribution feeder, are discussed. The results demonstrate that the proposed three-phase DOPF model and the heuristic solution method may yield some benefits to the LDCs in real-time optimal operation of distribution systems in the context of Smart Grids. This work also presents a mathematical model for optimal and real-time control of customer electricity usage, which can be readily integrated by industrial customers into their Energy Hub Management Systems (EHMSs). An Optimal Industrial Load Management (OILM) model is proposed, which minimizes energy costs and/or demand charges, considering comprehensive models of industrial processes, process interdependencies, storage units, process operating constraints, production requirements, and other relevant constraints. The OILM is integrated with the DOPF model to incorporate operating constraints required by the LDC system operator, thus combining voltage optimization with load control for additional benefits. The OILM model is applied to two industrial customers, i.e., a flour mill and a water pumping facility, and the results demonstrate the benefits to the industrial customers and LDCs that can be obtained by deploying the proposed OILM and three-phase DOPF models in EHMSs, in conjunction with Smart Grid technologies.1 yea

System Design of Internet-of-Things for Residential Smart Grid

Internet-of-Things (IoTs) envisions to integrate, coordinate, communicate, and collaborate real-world objects in order to perform daily tasks in a more intelligent and efficient manner. To comprehend this vision, this paper studies the design of a large scale IoT system for smart grid application, which constitutes a large number of home users and has the requirement of fast response time. In particular, we focus on the messaging protocol of a universal IoT home gateway, where our cloud enabled system consists of a backend server, unified home gateway (UHG) at the end users, and user interface for mobile devices. We discuss the features of such IoT system to support a large scale deployment with a UHG and real-time residential smart grid applications. Based on the requirements, we design an IoT system using the XMPP protocol, and implemented in a testbed for energy management applications. To show the effectiveness of the designed testbed, we present some results using the proposed IoT architecture.Comment: 10 pages, 6 figures, journal pape