Petra Heijnen

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.

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.

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

Designing Networked Energy Infrastructures with Architectural Flexibility

Development of networked energy infrastructures (like gas pipe networks), generally requires a significant amount of capital investment under resources, market and institutional uncertainties. Several independent suppliers and consumers are to be connected into these networks. However, the actual commitment of these parties and the capacities they require from the network can remain uncertain for a long time. This is a challenging task for development co-owners because decisions, such as network architectures, have to be made while uncertainty exists. In order to effectively explore through the design space and identify architecturally flexible designs addressing a view of capacity uncertainty, a simulation framework based on a combination of Monte Carlo simulation and Graph Theory is proposed. It integrates a stochastic capacity demand model and network design heuristic algorithm. The framework will be able to evaluate architectural design options and show that architectural flexibility can significantly improve the value of the infrastructure project by reducing downside risks and benefiting from upside gains compared to the deterministic design approach.

Better design and operation of infrastructures through bi-level decision making

The need for improvement of the design and operation of infrastructures, which are complex socio-technical systems, has created a demand on investigating if complex optimization methods are applicable in this area. This paper stresses the importance and applicability of multi-level optimization in the world of infrastructures. The advantages of multi-level programming in comparison to an isolated top-down decision problem are shown by an example of a distributed energy system with small micro CHP units. The paper concludes with remarks on the applicability of the presented approach to other infrastructure sectors and with ideas for future research.

Improving short-term planning by incorporating scheduling consequences

Abstract Decisions in short-term planning and scheduling in multi-product multi-purpose plants are often taken in isolation from each other and may be based on conflicting objectives. This may results in decisions that are sub-optimal for the plant as a whole. A more integral perspective on the decision-making could lead to a better overall performance. This paper describes a mathematical reformulation of the short-term planning and scheduling decisions in a bi-level top-down program. It is based on a recursive formula and a smart definition of the cutting rules, which greatly reduces the complexity of the optimization. A practical case shows that the reliability of the planning results increases significantly.

Dealing with uncertainty of demand in mid term planning

Nowadays, process industry has to operate in a fast changing environment and a highly dynamic market, requiring efficient decision-making on all levels of plant planning, scheduling and control based on uncertain information. This paper deals with a case of flexible production planning for a multi-product plant to optimise expected proceeds from product sales when facing uncertainty in the demands for existing and emerging new products over the planning period. The emphasis is on the applicability of the developed planning technique that should be compatible with the normal work processes and with the associated level of technical competence, ensuring that the input and output of the method is well understood and interpreted by the users.

Supporting the electricity network operator in the allocation process

In the current design of the electricity sector in the Netherlands, network operators daily receive data from measurement companies about the electricity consumption of their customers for each quarter of an hour. The network operator sorts the data per combination of Program Responsible Party (PRP) and network district region and sends the allocated data to the PRPs. The measurement series sometimes appear to contain errors. These errors need to be corrected in a short time. To avoid forwarding wrong data to the PRPs, the network operator would like to check the data on errors before they are sent. This paper shows that the electricity consumption can easily be predicted because the first-order autocorrelation of the data is very high. A simple prediction method is compared with the use of exponential smoothing models which give only slightly better results at best, but are much more complex to use.

A Novel Course on Integrated Batch-Plant Management

Abstract The course “Integrated Plant Management”, developed at the Department of Technology, Policy and Management at the Delft University of Technology, is aimed to provide knowledge and understanding of the plant operation in such a way that the challenges imposed by the economic, environmental and social sustainability are made more transparent (Verwater-Lukszo e.a., 2001). The course focuses on the batch processing industry, but the most concepts are applicable to continuous and discrete industry as well. The integration of the enterprise functions as strategic and tactical management, forecasting, planning, scheduling, recipe management, process execution, optimisation and control are central to the main course aim. To realise this integrated manner of plant management the modern concepts of manufacturing execution systems (MES), plant modelling according to the ISA-S88 and ISA-S95 standards, total quality management and system thinking are very useful. Those issues constitute the main focus of the course. The course is concluded by an emphasis on the importance of the integration programs for quality and environment, which can be realised and maintained according to the principles of the new ISO quality (ISO 9001:2000, 2000) and environmental (ISO14001: 1996, 1996) standards. Modelling enterprise activities and production processes as well as experimental and model-based process optimisation are the enablers of the intended improvements. Monitoring business and process performance form the next step. The principles of Statistical Quality Control form on one hand a basis for a sound assurance of quality and enterprise performance. On the other hand, they create a framework for continual performance improvement.