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The integration of renewable energy systems poses major challenges on distribution grid operators. Because of the strong growth rates of the installation of photovoltaic (PV) and wind generators, huge needs for reinforcements in grids are expected. Next to conventional reinforcements (with additional and/or bigger dimensioned cables and transformers) also the introduction of decentralized storage systems seems to be promising. In this paper, an economical approach is presented enabling the calculation of break-even points for storage systems as a substitute to conventional grid reinforcements. The dynamic profitability calculation considers main influencing cost drivers for both alternatives, including operational and capital expenditures. Furthermore, the calculation of benefits of decentralized storage systems for upstream grid levels is also integrated. To enable these calculations, a storage model is derived oriented on battery characteristics to determine main requirements of a storage system to be able to integrate renewable energy systems. These elaborations are reflected on a real-world distribution grid faced with reinforcement needs due to the integration of PV. For this, measured data for the PV generator are integrated as well. The analysis reveal break-even points for the storage asset ranging between 100 and 500 € per kWh of installed capacity, depending on the lifetime of the storage asset and the costs for the substitute. Furthermore, main influencing parameters are evaluated using a sensitivity analysis. It is shown that the profitability can be increased significantly if not all peaks of PV generation need to be stored. Furthermore, the analysis of the operation for 1 year indicates that a combined operation of the storage asset (not only oriented on grid objectives such as peak shaving, but considering also the objectives of further stakeholders such as energy traders) seems to be reasonable for increasing the profitability and incentivizing a larger market penetration of storage assets.

Increasing demand, extra fluctuation and a large share of distributed electricity generation will put more stress on the electricity supply chain. Therefore, changes are required in the supply chain to maintain a properly functioning, stable and affordable grid. Currently the supply chain is completely driven by demand by constantly adapting the production to the demand. The exploitation of flexibility on the demand side of the supply chain allows for a more efficient and sustainable electricity production. Techniques like controllable distributed generation, distributed storage, and smart appliances can introduce this required flexibility. To exploit the new flexibility, the grid has to become more intelligent, i.e. become a Smart Grid. In this thesis TRIANA, a three step control strategy for the Smart Grid is presented. In this first step of TRIANA, the forecasting step, the scheduling freedom (flexibility) of a device is determined for each individual device by a local controller. In the second step, a (central) planner tries to exploit the freedom of the devices determined in the first step for his objective. The last step of TRIANA is the real-time control step performed by the local controller achieving the planning in the best possible way. In order to analyze the impact of control methodologies for Smart Grid, a simulator based on an energy model has been built. The basic elements of the model are individual devices and between devices energy streams are defined. The energy streams are connected via so called pools, which represent the physical connections between the devices. To study the effectiveness of the control methodology and study the most economic use of the flexibility of devices, multiple scenarios have been simulated. Simulations show that TRIANA can optimize the energy flows and can control the operation of the domestic devices in an economic manner without discomfort for the residents. TRIANA is a methodology capable of adjusting the energy profiles. It can autonomously determine and exploit the optimization potential of a large group of buildings. The combination of forecasting, planning and realtime control is a promising direction for control methodologies for Smart Grids: it is scalable, generic and reliable.

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.

The paper discusses an Integer Linear Programming (ILP) formulation that describes the problem of planning the use of domestic distributed generators, under individual as well as fleet constraints. The planning problem comprises the assignment of time intervals during which the local generator must produce or not. In [1] this ILP is shown to be NP-complete in the strong sense. Heuristic methods have been developed to find solutions in reasonable time. In this work a different technique is used to overcome the complexity problems. We use column generation to search the possible decision vectors in a faster way. The ILP is slightly adjusted to facilitate the column generation technique to search in a clever way through the set of possible solutions. To measure the results, the column generation technique is compared to an earlier developed heuristic method. Both the quality of the objective function and the speed of the methods are compared.

In de Tweede Kamer laaide in 2008 een discussie op naar aanleiding van geluiden uit de samenleving over het wel of niet verplicht installeren van slimme meters in huizen. Voor- en tegenstanders van een verplichte invoer gebruiken ondertussen alle media om de algemene opinie te beïnvloeden. Voorstanders wijzen op de potentie van slimme meters voor energiebesparing. Tegenstanders roepen leuzen als ‘Slim meten is slinks weten’ en zijn waakzaam voor het zomaar vrijgeven van privacygevoelige informatie. Ondertussen wordt er al ingespeeld op toekomstige wetgeving en onderscheidt Oxxio zich van andere nutsbedrijven door bij iedere klant een slimme meter te installeren. Het elektriciteits- en gasverbruik kan vervolgens op de website bekeken worden. Aan de hand van deze gegevens kunnen klanten conclusies trekken over hun energieverbruik en zich bewuster worden van hun eigen kosten voor energieverbruik. De grenzen van de mogelijkheden van de slimme meter zijn hiermee echter nog lang niet bereikt: een slimme meter kan de interface worden van uitgebreidere besparingstechnieken. Dit hoofdstuk zal een antwoord proberen te geven op de vraag: wat maakt een slimme meter echt slim en wat is de potentie van een slimme meter?

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.

A change in future electricity grids is expected caused by the introduction of distributed generation, distributed storage and demand side load management. To analyse the impact of these technologies, a simulator has been developed. With this simulator, a small group of households with micro-generators can already be analysed. However, due to the large memory footprint, larger groups of houses cannot be simulated. In this paper an improved simulator which is capable of distributing simulations over multiple PCs via a network is presented. Using this distributed approach, more (memory) resources can be utilised and more calculations can be done in parallel. Although the introduction of the network stack gives some overhead, still a large speedup is seen when more PCs are used. Furthermore, far bigger groups of houses can simulated.

Distributed electricity generation is increasingly applied in the electricity grid. This generation can be more energy efficient than conventional generation; however, a large scale introduction of distributed generators implies possible instability in the grid. A Virtual Power Plant (VPP) deals with this problem by controlling the distributed generators in such a way, that the complete group acts like a normal power plant. All generators need to be steered individually, which makes the control more complex than the control of a normal power plant. The VPP we describe in this paper consists of a large number of microCHP (Combined Heat and Power) appliances. As the produced heat of such a microCHP is completely consumed locally, the production opportunities of the distributed generators are determined by the local heat demand. This heat demand can be predicted one day ahead, see e.g. [2]. A feasible schedule of a single generator ensures that the heat demand is supplied in time. The objective is to generate at the most profitable times. In [3] a dynamic programming formulation is described which schedules one generator optimally (for local objectives) within reasonable time. We extend this method to the case of a Virtual Power Plant. The costs of the basic state changes in the dynamic programming formulation of the individual generators are altered, according to the objective of the VPP, which is to generate a predefined electricity pattern for a full day. Starting from the point of view of local objectives of individual generators the global objective of the VPP is gradually (iteratively) incorporated. This results in a production planning that can be done in reasonable time, compared to the planning horizon.

Due to increasing energy prices and the greenhouse effect more efficient electricity production is desirable, referably based on renewable sources. In the last years, a lot of technologies have been developed to improve the efficiency of the electricity usage and supply. Next to large scale technologies such as windturbine parks, a lot of 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 methodologies optimizing the combination of techniques raise the potential of the individual techniques. A lot of research in done in this area. This paper outlines a number of papers and deducts the general idea. Next, 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 a field test verifying that methodology is promising.

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 signi��?cantly.

The growing awareness of the greenhouse gas effect and rising energy prices lead to more and more initiatives to improve energy ef��?ciency. These initiatives range from micro-generation, local energy storage and more ef��?cient appliances to controllers with optimization objectives. However, the introduction of these initiatives might have a signi��?cant impact on the current electricity infrastructure. Furthermore, it is dif��?cult to analyse the succes on the energy ef��?ciency of the introduction of (combinations of) these initiatives. Therefore, a simulator is develloped to analyse the impact of different combinations of micro-generators, energy buffers, appliances and control algorithms on the energy ef��?ciency, both within the house and on bigger scale. The simulator is easily adaptable for new types of micro-generators, controllers and other supported devices. 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.

The introduction of microCHP (Combined Heat and Power) appliances and other means of distributed generation causes a shift in the way electricity is produced and consumed. Households themselves produce electricity and deliver the surplus to the grid. In this way, the distributed generation also has implications on the transformers and, thus, on the grid. In this work we study the influence of introducing microCHP appliances on the total load of a group of houses (behind the last transformer). If this load can be controlled, the transformer may be relieved from peak loads. Moreover, a well controlled fleet production can be offered as a Virtual Power Plant to the electricity grid. In this work we focus on different algorithms to control the fleet and produce a constant electricity output. We assume that produced electricity is consumed as locally as possible (preferably within the household). Produced heat can only be consumed locally. Additionally, heat can be stored in heat stores. Fleet control is achieved by using heat led control algorithms and by specifying as objective how much of the microCHP appliances have to run. First results show that preferred patterns can be produced by using fleet control. However, as the problem is heat driven, still reasonably large deviations from the objective occur. Several combinations of heat store and fleet control algorithm parameters are considered to match the heat demand and supply. This work is a first attempt in controlling a fleet and gives a starting point for further research in this area. A certain degree of control can already be established, but for better stability more intelligent algorithms are needed.

In the coming decade a strong trend towards distributed electricity generation (microgeneration) is expected. Micro-generators are small appliances that generate electricity (and heat) at the kilowatt level, which allows them to be installed in households. By combining a group of micro-generators, a Virtual Power Plant can be formed. The electricity market/network requires a VPP control system to be fast, scalable and reliable. It should be able to adjust the production quickly, handle in the order of millions of micro-generators and it should ensure the required production is really produced by the fleet of microgenerators. When using micro Combined Heat and Power microgenerators, the electricity production is determined by heat demand. In this paper we propose a VPP control system design using learning systems to maximise the economical benefits of the microCHP appliances. Furthermore, ways to test our design are described.

Energy saving is a hot topic. People are getting more and more aware of their energy usage because of continuously rising energy prices and awareness of the greenhouse problem. One of the most important causes of the greenhouse effect is the massive emission of carbon dioxide (CO2). CO2 is produced when (fossil) fuel is burned, for example by cars, industry and (natural gas fueled) boilers for domestic heating. Rising energy prices and the need for CO2 emission reduction stimulate the research for new, more energy-efficient technologies. One of the innovative, energy-saving domestic appliances is the micro Combined Heat and Power (µCHP) [1]<>. µCHP appliances produce, besides heat, electricity with a total overall efficiency comparable to conventional high-efficiency boilers [1,2], which is around 90%. Energy is saved because the heat (a rest product of electricity production) is used. Heat produced in central power plants is mostly wasted, resulting in a maximum efficiency of 55% [2]. <> Replacing a high-efficiency boiler with a µCHP appliance causes a slight increase in natural gas usage, but the electricity import decreases. The benefit for an average Dutch family is a CO2 emission reduction of 1000kg and Є200 saving on the energy bill annually [2]. An average family currently causes an CO2 emission of 5000kg due to electricity and gas usage and 7700kg in total [2,3]. Households together are responsible for 20-25% of the total CO2 emission in the Netherlands[3]. The figures in this article are based on a Stirling based µCHP appliance, currently the only market-ready technology. The same principles are applicable for µCHP appliances based on other technologies (e.g. fuel cells) and other heat/electricity production rates.

The µCHP is expected as the successor of the conventional high-efficiency boiler producing next to heat also electricity with a comparable overall efficiency. A µCHP appliance saves money and reduces greenhouse gas emission. An additional functionality of the µCHP is using the appliance as a backupgenerator in case of a power outage. The µCHPcould supply the essential loads, the heating and reduce the discomfort up to a certain level. This requires modi��?cations on the µCHP appliance itself as well as on the domestic electricity infrastructure. Furthermore some extra hardware and a control algorithm for load balancing are necessary. Our load balancing algorithm is supposed to start and stop the µCHP and switch off loads if necessary. The ��?rst simulation results show that most of the electricity usage is under the maximum generation line, but to reduce the discomfort an electricity buffer is required.

In linear solvers, like the conjugate gradient algorithm, sparse-matrix vector multiplication is an important kernel. Due to the sparseness of the matrices, the solver runs relatively slow. For digital optical tomography (DOT), a large set of linear equations have to be solved which currently takes in the order of hours on desktop computers. Our goal was to speed up the conjugate gradient solver. In this paper we present the results of applying multiple optimization techniques and exploiting multi-core solutions offered by two recently introduced architectures: Intel's Woodcrest general purpose processor and NVIDIA's G80 graphical processing unit. Using these techniques for these architectures, a speedup of a factor three has been achieved.