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

Management and Control of Domestic Smart Grid Technology

Emerging new technologies like distributed generation, distributed storage, and demand-side load management will change the way we consume and produce energy. These techniques enable the possibility to reduce the greenhouse effect and improve grid stability by optimizing energy streams. By smartly applying future energy production, consumption, and storage techniques, a more energy-efficient electricity supply chain can be achieved. In this paper a three-step control methodology is proposed to manage the cooperation between these technologies, focused on domestic energy streams. In this approach, (global) objectives like peak shaving or forming a virtual power plant can be achieved without harming the comfort of residents. As shown in this work, using good predictions, in advance planning and real-time control of domestic appliances, a better matching of demand and supply can be achieved.\ud \u

Improved Heat Demand Prediction of Individual Households

One of the options to increase the energy efficiency of current electricity network is the use of a Virtual Power Plant. By using multiple small (micro)generators distributed over the country, electricity can be produced more efficiently since these small generators are more efficient and located where the energy is needed. In this paper we focus on micro Combined Heat and Power generators. For such generators, the production capacity is determined and limited by the heat demand. To keep the global electricity network stable, information about the production capacity of the heat-driven generators is required in advance. In this paper we present methods to perform heat demand prediction of individual households based on neural network techniques. Using different input sets and a so called sliding window, the quality of the predictions can be improved significantly. Simulations show that these improvements have a positive impact on controlling the distributed microgenerators

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

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

Steering the Smart Grid

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 and optimization methodologies were developed to increase the efficiency, maintain the grid stability and support large scale introduction of renewable sources. In previous work, we showed the effectiveness of our three-step methodology to reach these objectives, consisting of 1) offline prediction, 2) offline planning and 3) online scheduling in combination with MPC. In this paper we analyse the best structure for distributing the steering signals in the third step. Simulations show that pricing signals work as good as on/off signals, but pricing signals are more general. Individual pricing signals per house perform better with small prediction errors while one global steering signal for a group of houses performs better when the prediction errors are larger. The best hierarchical structure is to use consumption patterns on all levels except the lowest level and deduct the pricing signals in the lowest node of the tree

Controlling a group of microCHPs: planning and realization

This paper discusses the planning problem of a group of domestic Combined Heat and Power (microCHP) appliances, which together form a Virtual Power Plant (VPP). To act on an electricity trading market, this VPP has to specify a production plan for electricity for given times of the day to offer to this market. These amounts have to be delivered exactly when these times arrive; moreover, deviations from these contracts are penalized for. We focus on the planning of individual microCHPs for one day ahead, given that the aggregated output of the group should fulfill a desired production pattern that the VPP wants to offer on the market. The contribution in this context is twofold. Firstly, we present a planning approach based on column generation which calculates for all individual appliances production patterns. The production patterns are calculated such that the deviation of the agregated pattern of all appliances from a prespecified pattern is minimized. Secondly, we investigate how a desired pattern for the group can be specified based on global parameters and which patterns can be realized afterwards by the developed planning approach. In this way we get insight what kind of pattern may be offered on the market. The presented results show that we can find near optimal solutions using a column generation technique and that we can offer patterns with large variation on the market, as long as the running average does not deviate too much from the possible production

Improving stability and utilization of the electricity infrastructure of a neighbourhood

Increasing energy prices and the greenhouse effect lead to more awareness of energy efficiency of electricity supply.\ud During the last years, a lot of technologies and optimization methodologies are developed to increase the efficiency, maintain the grid stability and support large scale introduction of renewable sources.\ud In previous work, we showed the effectiveness of our three-step methodology to reach these objective, consisting of 1) off-line prediction, 2) off-line planning and 3) online scheduling.\ud \ud The goal of this paper is 1) to analyze the impact of installing a local controller in the house and 2) to analyze the stabilizing effect of the optimization algorithms on a large group of houses.\ud To investigate whether it is possible to develop a local controller, a proof-of-concept is built using an embedded PC.\ud The prototype consumes significantly less power than it can save.\ud The stabilizing effect is studied by two large scale use cases.\ud The first one is a simulation of 200 houses that together try to respond on fluctuation in generation of a windmill park.\ud The second one is a simulation of a fleet of 100 electrical cars that need to be charged at night.\ud Using the three step methodology in these two scenarios, the required balancing power, peaks and fluctuations in the required generation of the power plants decrease up to 40%

Simulation of the effect of introducing micro-generation, energy buffers and accompanied optimization algorithms on the energy efficiency

The growing awareness of the greenhouse gas effect and rising energy prices lead to more and more initiatives to improve energy efficiency. These initiatives range from micro-generation, local energy storage and more efficient appliances to controllers with optimization objectives. However, the introduction of these initiatives might have a significant impact on the current electricity infrastructure. Furthermore, it is difficult to analyse the succes on the energy efficiency of the introduction of (combinations of) these initiatives.\ud Therefore, a simulator is develloped to analyse the impact\ud of different combinations of micro-generators, energy buffers, appliances and control algorithms on the energy efficiency, both within the house and on bigger scale. The simulator is easily adaptable for new types of micro-generators, controllers and other supported devices.\ud First simulations with the simulator show that the results are correct and promising. However, especially the usage of resources during the simulation has to decrease.\u