A Three-Step Methodology to Improve Domestic Energy Efficiency

Increasing energy prices and the greenhouse effect lead to more awareness of energy efficiency of electricity supply. During the last years, a lot of technologies have been developed to improve this efficiency. Next to large scale technologies such as windturbine parks, domestic technologies are developed. These domestic technologies can be divided in 1) Distributed Generation (DG), 2) Energy Storage and 3) Demand Side Load Management. Control algorithms optimizing a combination of these techniques can raise the energy reduction potential of the individual techniques. In this paper an overview of current research is given and a general concept is deducted. Based on this concept, a three-step optimization methodology is proposed using 1) offline local prediction, 2) offline global planning and 3) online local scheduling. The paper ends with results of simulations and field tests showing that the methodology is promising.\u

Islanded house operation using a micro CHP

The µCHP is expected as the successor of\ud the conventional high-efficiency boiler producing next to\ud heat also electricity with a comparable overall efficiency.\ud A µCHP appliance saves money and reduces greenhouse\ud gas emission.\ud An additional functionality of the µCHP is using the\ud appliance as a backupgenerator in case of a power outage.\ud The µCHPcould supply the essential loads, the heating and\ud reduce the discomfort up to a certain level. This requires\ud modifications on the µCHP appliance itself as well as on\ud the domestic electricity infrastructure. Furthermore some\ud extra hardware and a control algorithm for load balancing\ud are necessary.\ud Our load balancing algorithm is supposed to start and\ud stop the µCHP and switch off loads if necessary. The first\ud simulation results show that most of the electricity usage\ud is under the maximum generation line, but to reduce the\ud discomfort an electricity buffer is required.\u

Hard- and software implementation and verification of an Islanded House prototype

Abstract: Rising energy prices and the greenhouse effect gave a boost to the innovation of energy saving technologies. One of these technologies is microCHP, a replacement of a boiler producing heat and electricity. We investigated whether it is possible to use a microCHP to decrease discomfort during a power cut by supplying the most important appliances, creating a so called Islanded House. Simulations showed that the discomfort can be decreased when also a battery is added. A prototype is used to justify the assumptions made for the simulation. Finally, one of the control algorithms used in the simulations is implemented as controller for the prototype. Based on these results we conclude that it is possible to create an Islanded House and to decrease the discomfort significantly.\u

Decentralized Demand Side Management with Rooftop PV in Residential Distribution Network

In the past extensive researches have been conducted on demand side management (DSM) program which aims at reducing peak loads and saving electricity cost. In this paper, we propose a framework to study decentralized household demand side management in a residential distribution network which consists of multiple smart homes with schedulable electrical appliances and some rooftop photovoltaic generation units. Each smart home makes individual appliance scheduling to optimize the electric energy cost according to the day-ahead forecast of electricity prices and its willingness for convenience sacrifice. Using the developed simulation model, we examine the performance of decentralized household DSM and study their impacts on the distribution network operation and renewable integration, in terms of utilization efficiency of rooftop PV generation, overall voltage deviation, real power loss, and possible reverse power flows.Comment: 5 pages, 7 figures, ISGT 2018 conferenc

Control and Communication Protocols that Enable Smart Building Microgrids

Recent communication, computation, and technology advances coupled with climate change concerns have transformed the near future prospects of electricity transmission, and, more notably, distribution systems and microgrids. Distributed resources (wind and solar generation, combined heat and power) and flexible loads (storage, computing, EV, HVAC) make it imperative to increase investment and improve operational efficiency. Commercial and residential buildings, being the largest energy consumption group among flexible loads in microgrids, have the largest potential and flexibility to provide demand side management. Recent advances in networked systems and the anticipated breakthroughs of the Internet of Things will enable significant advances in demand response capabilities of intelligent load network of power-consuming devices such as HVAC components, water heaters, and buildings. In this paper, a new operating framework, called packetized direct load control (PDLC), is proposed based on the notion of quantization of energy demand. This control protocol is built on top of two communication protocols that carry either complete or binary information regarding the operation status of the appliances. We discuss the optimal demand side operation for both protocols and analytically derive the performance differences between the protocols. We propose an optimal reservation strategy for traditional and renewable energy for the PDLC in both day-ahead and real time markets. In the end we discuss the fundamental trade-off between achieving controllability and endowing flexibility

Supporting high penetrations of renewable generation via implementation of real-time electricity pricing and demand response

The rollout of smart meters raises the prospect that domestic customer electrical demand can be responsive to changes in supply capacity. Such responsive demand will become increasingly relevant in electrical power systems, as the proportion of weather-dependent renewable generation increases, due to the difficulty and expense of storing electrical energy. One method of providing response is to allow direct control of customer devices by network operators, as in the UK 'Economy 7' and 'White Meter' schemes used to control domestic electrical heating. However, such direct control is much less acceptable for loads such as washing machines, lighting and televisions. This study instead examines the use of real-time pricing of electricity in the domestic sector. This allows customers to be flexible but, importantly, to retain overall control. A simulation methodology for highlighting the potential effects of, and possible problems with, a national implementation of real-time pricing in the UK domestic electricity market is presented. This is done by disaggregating domestic load profiles and then simulating price-based elastic and load-shifting responses. Analysis of a future UK scenario with 15 GW wind penetration shows that during low-wind events, UK peak demand could be reduced by 8-11 GW. This could remove the requirement for 8-11 GW of standby generation with a capital cost of £2.6 to £3.6 billion. Recommended further work is the investigation of improved demand-forecasting and the price-setting strategies. This is a fine balance between giving customers access to plentiful, cheap energy when it is available, but increasing prices just enough to reduce demand to meet the supply capacity when this capacity is limited

From Packet to Power Switching: Digital Direct Load Scheduling

At present, the power grid has tight control over its dispatchable generation capacity but a very coarse control on the demand. Energy consumers are shielded from making price-aware decisions, which degrades the efficiency of the market. This state of affairs tends to favor fossil fuel generation over renewable sources. Because of the technological difficulties of storing electric energy, the quest for mechanisms that would make the demand for electricity controllable on a day-to-day basis is gaining prominence. The goal of this paper is to provide one such mechanisms, which we call Digital Direct Load Scheduling (DDLS). DDLS is a direct load control mechanism in which we unbundle individual requests for energy and digitize them so that they can be automatically scheduled in a cellular architecture. Specifically, rather than storing energy or interrupting the job of appliances, we choose to hold requests for energy in queues and optimize the service time of individual appliances belonging to a broad class which we refer to as "deferrable loads". The function of each neighborhood scheduler is to optimize the time at which these appliances start to function. This process is intended to shape the aggregate load profile of the neighborhood so as to optimize an objective function which incorporates the spot price of energy, and also allows distributed energy resources to supply part of the generation dynamically.Comment: Accepted by the IEEE journal of Selected Areas in Communications (JSAC): Smart Grid Communications series, to appea

Smart Microgrids: Overview and Outlook

The idea of changing our energy system from a hierarchical design into a set of nearly independent microgrids becomes feasible with the availability of small renewable energy generators. The smart microgrid concept comes with several challenges in research and engineering targeting load balancing, pricing, consumer integration and home automation. In this paper we first provide an overview on these challenges and present approaches that target the problems identified. While there exist promising algorithms for the particular field, we see a missing integration which specifically targets smart microgrids. Therefore, we propose an architecture that integrates the presented approaches and defines interfaces between the identified components such as generators, storage, smart and \dq{dumb} devices.Comment: presented at the GI Informatik 2012, Braunschweig Germany, Smart Grid Worksho

Domestic energy management methodology for optimizing efficiency in Smart Grids

Increasing energy prices and the greenhouse effect lead to more awareness of energy efficiency of electricity supply. During the last years, a lot of domestic technologies have been developed to improve this efficiency. These technologies on their own already improve the efficiency, but more can be gained by a combined management. Multiple optimization objectives can be used to improve the efficiency, from peak shaving and Virtual Power Plant (VPP) to adapting to fluctuating generation of wind turbines. In this paper a generic management methology is proposed applicable for most domestic technologies, scenarios and optimization objectives. Both local scale optimization objectives (a single house) and global scale optimization objectives (multiple houses) can be used. Simulations of different scenarios show that both local and global objectives can be reached