Algorithms for balancing demand-side load and micro-generation in Islanded Operation

Micro-generators are devices installed in houses pro-\ud ducing electricity at kilowatt level. These appliances can\ud increase energy efficiency significantly, especially when\ud their runtime is optimized. During power outages micro-\ud generators can supply critical systems and decrease dis-\ud comfort.\ud In this paper a model of the domestic electricity infras-\ud tructure of a house is derived and first versions of algo-\ud rithms for load/generation balancing during a power cut\ud are developed. In this context a microCHP device, produc-\ud ing heat and electricity at the same time with a high effi-\ud ciency, is used as micro-generator.\ud The model and the algorithms are incorporated in a sim-\ud ulator, which is used to study the effect of the algorithms for\ud load/generation balancing. The results show that with some\ud extra hardware all appliances in a house can be supplied,\ud however not always at the preferred time.\u

Load control in low voltage level of the electricity grid using µCHP appliances

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.\ud \ud 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.\ud \ud 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.\ud \ud 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

Implementation of a 2-D 8x8 IDCT on the Reconfigurable Montium Core

This paper describes the mapping of a two-dimensional inverse discrete cosine transform (2-D IDCT) onto a wordlevel reconfigurable Montium Processor. This shows that the IDCT is mapped onto the Montium tile processor (TP) with reasonable effort and presents performance numbers in terms of energy consumption, speed and silicon costs. The Montium results are compared with the IDCT implementation on three other architectures: TI DSP, ASIC and ARM

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.\ud 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.\ud 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.\u

On the microCHP scheduling problem

In this paper both continuous and discrete models for the microCHP (Combined Heat and Power) scheduling problem are derived. This problem consists of the decision making to plan runs for a specific type of distributed electricity\ud generators, the microCHP. As a special result, one model variant of the problem, named n-DSHSP-restricted, is proven to be NP-complete in the strong sense. This shows the necessity of the development of heuristics for the scheduling of microCHPs, in case multiple generators are combined in a so-called fleet

Using heat demand prediction to optimise Virtual Power Plant production capacity

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\ud described

Islanded house operation using a micro CHP

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

Demand side load management using a three step optimization methodology

In order to keep a proper functional electricity grid and to prevent large investments in the current grid, the creation, transmission and consumption of electricity needs to be controlled and organized in a different way as done nowadays. Smart meters, distributed generation and -storage and demand side management are novel technologies introduced to reach a sustainable, more efficient and reliable electricity supply. Although these technologies are very promising to reach these goals, coordination between these technologies is required. It is therefore expected that ICT is going to play an important role in future smart grids. In this paper, we present the results of our three step control strategy designed to optimize the overall energy efficiency and to increase the amount of generation based on renewable resources with the ultimate goal to reduce the CO2 emission resulting from generation electricity. The focus of this work is on the control algorithms used to reshape the energy demand profile of a large group of buildings and their requirements on the smart grid. In a use case, steering a large group of freezers, we are able to reshape a demand profile full of peaks to a nicely smoothed demand profile, taking into the account the amount of available communication bandwidth and exploiting the available computation power distributed in the grid

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