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

OPERATIONAL PLANNING IN COMBINED HEAT AND POWER SYSTEMS

This dissertation presents methodologies for operational planning in Combined Heat and Power (CHP) systems. The subject of experimentation is the University of Massachusetts CHP system, which is a 22 MWe/640 MBh system for a district energy application. Systems like this have complex energy flow networks due to multiple interconnected thermodynamic components like gas and steam turbines, boilers and heat recovery steam generators and also interconnection with centralized electric grids. In district energy applications, heat and power requirements vary over 24 hour periods (planning horizon) due to changing weather conditions, time-of-day factors and consumer requirements. System thermal performance is highly dependent on ambient temperature and operating load, because component performances are nonlinear functions of these parameters. Electric grid charges are much higher for on-peak than off-peak periods, on-site fuel choices vary in prices and cheaper fuels are available only in limited quantities. In order to operate such systems in energy efficient, cost effective and least polluting ways, optimal scheduling strategies need to be developed. For such problems, Mixed-Integer Nonlinear Programming (MINLP) formulations are proposed. Three problem formulations are of interest; energy optimization, cost optimization and emission optimization. Energy optimization reduces system fuel input based on component nonlinear efficiency characteristics. Cost optimization addresses price fluctuations between grid on-peak and off-peak periods and differences in on-site fuel prices. Emission optimization considers CO2 emission levels caused by direct utilization of fossil fuels on-site and indirect utilization when importing electricity from the grid. Three solution techniques are employed; a deterministic algorithm, a stochastic search and a heuristic approach. The deterministic algorithm is the classical branch-and-bound method. Numerical experimentation shows that as planning horizon size increases linearly, computer processing time for branch-and-bound increases exponentially. Also in the problem formulation, fuel availability limitations lead to nonlinear constraints for which branch-and-bound in unable to find integer solutions. A genetic algorithm is proposed in which genetic search is applied only on integer variables and gradient search is applied on continuous variables. This hybrid genetic algorithm finds more optimal solutions than branch-and-bound within reasonable computer processing time. The heuristic approach fixes integer values over the planning horizon based on constraint satisfaction. It then uses gradient search to find optimum continuous variable values. The heuristic approach finds more optimal solutions than the proposed genetic algorithm and requires very little computer processing time. A numerical study using actual system operation data shows optimal scheduling can improve system efficiency by 6%, reduce cost by 11% and emission by 14%

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

Geothermal district heating networks: modelling novel operational strategies incorporating heat storage

The value of integrating a heat storage into a geothermal district heating system has been investigated. The behaviour of the system under a novel operational strategy has been simulated focusing on the energetic, economic and environmental effects of the new strategy of incorporation of the heat storage within the system. A typical geothermal district heating system consists of several production wells, a system of pipelines for the transportation of the hot water to end-users, one or more re-injection wells and peak-up devices (usually fossil-fuel boilers). Traditionally in these systems, the production wells change their production rate throughout the day according to heat demand, and if their maximum capacity is exceeded the peak-up devices are used to meet the balance of the heat demand. In this study, it is proposed to maintain a constant geothermal production and add heat storage into the network. Subsequently, hot water will be stored when heat demand is lower than the production and the stored hot water will be released into the system to cover the peak demands (or part of these). It is not intended to totally phase-out the peak-up devices, but to decrease their use, as these will often be installed anyway for back-up purposes. Both the integration of a heat storage in such a system as well as the novel operational strategy are the main novelties of this thesis. A robust algorithm for the sizing of these systems has been developed. The main inputs are the geothermal production data, the heat demand data throughout one year or more and the topology of the installation. The outputs are the sizing of the whole system, including the necessary number of production wells, the size of the heat storage and the dimensions of the pipelines amongst others. The results provide several useful insights into the initial design considerations for these systems, emphasizing particularly the importance of heat losses. Simulations are carried out for three different cases of sizing of the installation (small, medium and large) to examine the influence of system scale. In the second phase of work, two algorithms are developed which study in detail the operation of the installation throughout a random day and a whole year, respectively. The first algorithm can be a potentially powerful tool for the operators of the installation, who can know a priori how to operate the installation on a random day given the heat demand. The second algorithm is used to obtain the amount of electricity used by the pumps as well as the amount of fuel used by the peak-up boilers over a whole year. These comprise the main operational costs of the installation and are among the main inputs of the third part of the study. In the third part of the study, an integrated energetic, economic and environmental analysis of the studied installation is carried out together with a comparison with the traditional case. The results show that by implementing heat storage under the novel operational strategy, heat is generated more cheaply as all the financial indices improve, more geothermal energy is utilised and less fuel is used in the peak-up boilers, with subsequent environmental benefits, when compared to the traditional case. Furthermore, it is shown that the most attractive case of sizing is the large one, although the addition of the heat storage most greatly impacts the medium case of sizing. In other words, the geothermal component of the installation should be sized as large as possible. This analysis indicates that the proposed solution is beneficial from energetic, economic, and environmental perspectives. Therefore, it can be stated that the aim of this study is achieved in its full potential. Furthermore, the new models for the sizing, operation and economic/energetic/environmental analyses of these kind of systems can be used with few adaptations for real cases, making the practical applicability of this study evident. Having this study as a starting point, further work could include the integration of these systems with end-user demands, further analysis of component parts of the installation (such as the heat exchangers) and the integration of a heat pump to maximise utilisation of geothermal energy