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

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

Applying Column Generation to the Discrete Fleet Planning Problem

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.\ud \ud 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.\ud \ud 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

Improved simulator to analyse the impact of distributed generation on the electricity grid

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.\ud 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

Asynchronous event driven distributed energy management using profile steering

Distributed Energy Management methodologies with a scheduling approach based on predictions require means to avoid problems related to prediction errors. Various approaches deal with such prediction errors by applying a different online control mechanism, such as a double-sided auction. However, this results in two separate control mechanisms for the planning phase and the real-time control phase. In this paper, we present a two-phase approach with profile steering based control in both phases. The first phase is synchronous and uses predictions to create a planning. The second phase uses profile steering to schedule individual devices in an event driven and asynchronous manner. Simulation results show that this methodology results in an improved power quality and follows the planning better with a RMSE reduction of up to 34%. In addition, it provides more robustness to failure of connection and improves transparency of its actions to prosumers

Comparative analysis of tertiary control systems for smart grids using the Flex Street model

Various smart grid control systems have been developed with different architectures. Comparison helps developers identify their strong and weak points. A three-step analysis method is proposed to facilitate the comparison of independently developed control systems. In the first step, a microgrid model is created describing demand and supply patterns of controllable and non-controllable devices (Flex Street). In the second step, a version of Flex Street is used to design a case, with a given control objective and key performance indicators. In the last step, simulations of different control systems are performed and their results are analysed and compared. The Flex Street model describes a diverse set of households based on realistic data. Furthermore, its bottom-up modelling approach makes it a flexible tool for designing cases. Currently, three cases with peak-shaving objectives are developed based on scenarios of the Dutch residential sector, specifying various penetration rates of renewable and controllable devices. The proposed method is demonstrated by comparing IntelliGator and TRIANA, two independently developed control systems, on peak reduction, energy efficiency, savings and abated emissions. Results show that IntelliGator---a real-time approach---is proficient in reducing peak demand, while TRIANA---a planning approach---also levels intermediate demand. Both systems yield benefits (\geneuro5--54 per household per year) through reduced transport losses and network investments in the distribution network

Cascaded column generation for scalable predictive demand side management

We propose a nested Dantzig-Wolfe decomposition, combined with dynamic programming, for the distributed scheduling of a large heterogeneous fleet of residential appliances with nonlinear behavior. A cascaded column generation approach gives a scalable optimization strategy, provided that the problem has a suitable structure. The presented approach extends the TRIANA smart grid framework for predictive demand side management; the main goal of this framework is peak shaving. Simulations validate that the approach is effective, but also show that the performance degrades for smaller group sizes