Michiel Houwing

Demand Response With Micro-CHP Systems

With the increasing application of distributed energy resources and novel information technologies in the electricity infrastructure, innovative possibilities to incorporate the demand side more actively in power system operation are enabled. A promising, controllable, residential distributed generation technology is a microcombined heat and power system (micro-CHP). Micro-CHP is an energy-efficient technology that simultaneously provides heat and electricity to households. In this paper, we investigate to what extent domestic energy costs could be reduced with intelligent, price-based control concepts (demand response). Hereby, first the performance of a standard, so-called heat-led micro-CHP system is analyzed. Then, a model-predictive control (MPC) strategy aimed at demand response is proposed for more intelligent control of micro-CHP systems. Simulation studies illustrate the added value of the proposed intelligent control approach over the standard approach in terms of reduced variable energy costs. Demand response with micro-CHP lowers variable costs for households by about 1%-14%. The cost reductions are highest with the most strongly fluctuating real-time pricing scheme.

Least-cost model predictive control of residential energy resources when applying μmCHP

With an increasing use of distributed energy resources and intelligence in the electricity infrastructure, the possibilities for minimizing costs of household energy consumption increase. Technology is moving toward a situation in which households manage their own energy generation and consumption, possibly in cooperation with each other. As a first step, in this paper a decentralized controller based on model predictive control is proposed. For an individual household using a micro combined heat and power (muCHP) plant in combination with heat and electricity storages the controller determines what the actions are that minimize the operational costs of fulfilling residential electricity and heat requirements subject to operational constraints. Simulation studies illustrate the performance of the proposed control scheme, which is substantially more cost effective compared with a control approach that does not include predictions on the system it controls.

Agent-Based Control of Distributed Electricity Generation with Micro Combined Heat and Power: Cross-Sectoral Learning for Process and Infrastructure Engineers

For the distributed control of an electricity infrastructure incorporating clusters of residential combined heat and power units (micro-CHP or ?CHP) a Multi-Agent System approach is considered. The network formed by households generating electricity with ?CHP units and the facilitating energy supplier can be regarded as an electricity production system, analogous to a (flexible) manufacturing system. Next, the system boundary is extended by allowing the trade of electricity between networks of households and their supplier. A methodology for designing an agent-based system for manufacturing control is applied to both cases, resulting in a conceptual design for a control system for the energy infrastructure. Because of the analogy between production systems and infrastructures Process Systems Engineering (PSE) approaches for optimisation and control can be applied to infrastructure system operations. At the same time we believe research on socio-technical infrastructure systems will be a valuable contribution to PSE management strategies.

Socio-Technical Complexity in Energy Infrastructures Conceptual Framework to Study the Impact of Domestic Level Energy Generation, Storage and Exchange

Household level energy conversion, storage and exchange technologies are assumed to pervade the energy infrastructure in the future. These novel technologies will influence the total infrastructure in a bottom-up way; both technically and socially. Not only the physical networks, but also the social actor network consisting of households, network managers, energy suppliers and producers is influenced. This paper describes and conceptualizes a complex systems approach towards energy infrastructures based on a large penetration of decentralized technologies. Households thereby contain an energy hub; an interface between a number of energy sources and loads. Households can interact with each other and with other actors via their hubs. Our approach paves the way for modelling the socio-technical complexity via agent-based modelling (ABM) and for subsequent exploratory simulations.

Balancing wind power with virtual power plants of micro-CHPs

Higher participation levels of wind power in power systems will increase the need for flexible back-up generation to balance the differences between predicted and realized wind power production. This is often an expensive solution. With distributed energy resources and more ICT at the demand side, novel, and possibly cheaper, ways for imbalance minimization arise. Micro combined heat-and-power (micro-CHP) is a novel domestic-level generation technology, producing heat and power simultaneously. Clusters of micro-CHPs can function as flexible virtual power plants (VPPs). This paper presents the design of an online coordination scheme that can substantially reduce the imbalance volumes and the associated costs for wind power traders by actively controlling a VPP comprising micro-CHP systems. It is shown that the imbalance volume and associated cost can be reduced by 73 % and 38 %, respectively.

Model predictive control for residential energy resources using a mixed-logical dynamic model

With the increase in the number of distributed energy resources and the amount of intelligence in electricity infrastructures, the possibilities for minimizing costs of household energy consumption increase. Household systems are hybrid systems, in the sense that they exhibit both continuous and discrete dynamics. In this paper the mixed-logical dynamic framework is used to construct a dynamic model of a household system equipped with distributed energy resources. A model predictive controller (MPC) is then proposed that uses the mixed-logical dynamic model to control the energy flows inside the household. In simulation studies we assess the performance of the proposed controller, and we illustrate how additional profits can be obtained by increasing the decision freedom of the controller.

Adaptive prediction model accuracy in the control of residential energy resources

With the increasing use of distributed energy resources and intelligence in the electricity infrastructure, the possibilities for minimizing costs of household energy consumption increase. Technology is moving toward a situation in which automated energy management systems could control domestic energy generation, storage, and consumption. In previous work we have proposed a controller based on model predictive control for controlling an individual household using a micro combined heat and power plant in combination with heat and electricity storages. Although the controller provides adequate performance in computer simulations, the computational time required to determine which actions to take can be significant, due to the precise predictions made over a long prediction horizon. In this paper we propose to make the computations less time consuming by coarsening the quality of the predictions made over the prediction horizon by decreasing their time resolution. In simulation studies we illustrate the performance of the proposed approach.

Model predictive control of fuel cell micro cogeneration systems

With the increasing application of distributed energy resources and information technologies in the electricity infrastructure, innovative possibilities for incorporating the demand side more actively in power system operation are enabled. At the residential level energy costs could be reduced with intelligent price-based control concepts (demand response). A promising, controllable, residential distributed generation technology is micro cogeneration (micro-CHP). Micro-CHP is an energy efficient technology that simultaneously provides heat and electricity to households during operation. This paper presents a detailed model of a household using a proton exchange membrane fuel cell (PEMFC) micro-CHP system in conjunction with heat storage options to fulfil its heat and part of its electricity demand. Furthermore, a decentralised controller based on a model predictive control (MPC) strategy is proposed. MPC can take benefit of future knowledge on prizes and energy demands and can therefore lead to better system performance. In simulations the performance of the MPC-controlled PEMFC system is illustrated under different conditions regarding energy pricing, domestic energy demand, and system configuration.

Deciding on Micro-CHP; A Multi-Level Decision-Making Approach

Micro combined heat and power (micro-CHP) is a promising, more fuel efficient, technology that could change the energy infrastructure as a whole. This paper describes the possible decision-making that results from micro-CHP introduction. The focus lies on the supplier-household interaction. Decisions made by supplier (price of electricity to/from households) influence decisions of households (1. micro-CHP power level and 2. amount of discharged heat) and determine the suppliers operational costs. When the supplier takes into account the cost optimization of households (that is based on the suppliers decision) in making his price-setting decisions, the problem can be described as a multi-level decision-making (MLDM) problem. We describe how the problem can be modelled and present a solution strategy which considers a combination of two objective functions that are subject to a set of constraints. Results of supplier price-setting are presented as well. Solving the problem via the MLDM approach is expected to lead to improved decision-making and a better performance of the supplier. Applying MLDM to the decision problem presented here is novel and can contribute to dealing with decision-making complexity in the energy infrastructure in general

Modelling an electricity infrastructure as a multi-agent system — Lessons learnt from manufacturing control

Abstract To model the control of an electricity infrastructure incorporating domestic level combined heat and power units (micro-CHP) a Multi-Agent System (MAS) approach is considered. This approach has already successfully been used to control manufacturing systems in the process industry. Because similarities between manufacturing and electricity generation exist it is interesting to investigate how a MAS methodology designed for manufacturing systems is applied to an electricity infrastructure. The interaction between energy companies and households is viewed here in a novel way, namely as a production process. By using an existing methodology for manufacturing control to design an agent-based controller for an electricity infrastructure, issues can be identified that have to be addressed in a control methodology specific for infrastructures.