M.G.C. Bosman

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.

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.

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.

On simulating the effect on the energy efficiency of smart grid technologies

Most residential-used electricity is nowadays generated at inefficient central power plants consuming environmental unfriendly resources like coal or natural gas. However, a trend towards distributed generation, distributed storage and demand side load management is seen to improve the energy efficiency. In order to analyze the impact and requirements of these emerging technologies and control methodologies, good simulation models and software is required. In this paper, an improved simulator is presented to model (domestic) energy usage to analyze control strategies and improved technology on the system as a whole. Compared to the previous model, this model is more expressive and allows more future scenarios to be analyzed. Due to the added complexity, the model is extended such that the simulation can be distributed over multiple computers to reduce simulation time.

Value of Storage in Distribution Grids—Competition or Cooperation of Stakeholders?

The implementation of storage capacities in distribution grids is seen as an important element for the integration of fluctuating feed-in caused by photovoltaic and wind generators. However, the responsibility for the operating of these assets is not defined in most market designs. Since decreasing costs are to be expected with further market penetration, next to distribution grid operators (DSO) further storage stake holders may be interested in controlling local storage devices. In this paper optimal storage profiles for different stakeholders (DSO and energy traders) are derived based on a case study with real world data. The results reveal conflicting interests-peak shaving of fluctuating feed-in (objective o the DSO to avoid reinforcements) is hampered significantly by storage usage of trading companies (objective of exploiting price spreads in the spot market). It is shown that unreasonable high costs occur with undesired economical side-effects if no control or cooperation mechanism is implemented.

Planning the production of a fleet of domestic combined heat and power generators

This paper describes a planning problem, arising in the energy supply chain, that deals with the planning of the production runs of micro combined heat and power (microCHP) appliances installed in houses, cooperating in a fleet. Two types of this problem are described. The first one is the Single House Planning Problem (SHPP), where the focus is on supplying heat in the household. The second one combines many microCHPs into a Fleet Planning Problem (FPP) and focuses on the mutual electricity output, while still considering the local heat demand in the individual households. The problem is modeled as an ILP. For practical use a local search method is developed for the FPP, based on a dynamic programming formulation of the SHPP.

The microCHP scheduling problem

The increasing penetration of renewable energy sources, the demand for more energy efficient electricity production and the increase in distributed electricity generation causes a shift in the way electricity is produced and consumed. The downside of these changes in the electricity grid is that network stability and controllability become more difficult compared to the old situation. The new network has to accommodate various means of production, consumption and buffering and needs to offer control over the energy flows between these three elements. In order to offer such a control mechanism we need to know more about the individual aspects. In this paper we focus on the modelling of distributed production. Especially, we look at the use of microCHP (Combined Heat and Power) appliances in a group of houses. The problem of planning the production runs of the microCHP is modelled via an ILP formulation, both for a single house and for a group of houses.

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.

Scheduling Microchps in a Group of Houses

The increasing penetration of renewable energy sources, the demand for more energy efficient electricity production and the increase in distributed electricity generation causes a shift in the way electricity is produced and consumed. The downside of these changes in the electricity grid is that network stability and controllability become more difficult compared to the old situation. The new network has to accommodate various means of production, consumption and buffering and needs to offer control over the energy flows between these three elements. In order to offer such a control mechanism we need to know more about the individual aspects. In this paper we focus on the modelling of distributed production. Especially, we look at the use of microCHP (Combined Heat and Power) appliances in a group of houses. The problem of planning the production runs of the microCHP is modelled via an ILP formulation, both for a single house and for a group of houses.

Benchmarking set for domestic smart grid management

In this paper we propose a benchmark for domestic smart grid management. It consists of an in-depth description of a domestic smart grid, in which local energy consumers, producers and buffers can be controlled. First, from this description a general benchmark framework is derived, which can be used as a guideline to create benchmark sets to compare domestic smart grid management methodologies. Secondly, an implementation of such a benchmark set is discussed in full detail, to give an example on how to use the framework to create a benchmark set. The application area and validity of a benchmark set can be clearly defined and checked, by using the general framework.