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

Management and Control of Domestic Smart Grid Technology

Emerging new technologies like distributed generation, distributed storage, and demand-side load management will change the way we consume and produce energy. These techniques enable the possibility to reduce the greenhouse effect and improve grid stability by optimizing energy streams. By smartly applying future energy production, consumption, and storage techniques, a more energy-efficient electricity supply chain can be achieved. In this paper a three-step control methodology is proposed to manage the cooperation between these technologies, focused on domestic energy streams. In this approach, (global) objectives like peak shaving or forming a virtual power plant can be achieved without harming the comfort of residents. As shown in this work, using good predictions, in advance planning and real-time control of domestic appliances, a better matching of demand and supply can be achieved.\ud \u

Distributed smart charging of electric vehicles for balancing wind energy

To meet worldwide goals of reducing CO2 footprint, electricity production increasingly is stemming from so-called renewable sources. To cater for their volatile behavior, so-called demand response algorithms are required. In this paper, we focus particularly on how charging electrical vehicles (EV) can be coordinated to maximize green energy consumption. We present a distributed algorithm that minimizes imbalance costs, and the disutility experienced by consumers. Our approach is very much practical, as it respects privacy, while still obtaining near-optimal solutions, by limiting the information exchanged: i.e. consumers do not share their preferences, deadlines, etc. Coordination is achieved through the exchange of virtual prices associated with energy consumption at certain times. We evaluate our approach in a case study comprising 100 electric vehicles over the course of 4 weeks, where renewable energy is supplied by a small scale wind turbine. Simulation results show that 68% of energy demand can be supplied by wind energy using our distributed algorithm, compared to 73% in a theoretical optimum scenario, and only 40% in an uncoordinated business-as-usual (BAU) scenario. Also, the increased usage of renewable energy sources, i.e. wind power, results in a 45% reduction of CO2 emissions, using our distributed algorithm

Improved Heat Demand Prediction of Individual Households

One of the options to increase the energy efficiency of current electricity network is the use of a Virtual Power Plant. By using multiple small (micro)generators distributed over the country, electricity can be produced more efficiently since these small generators are more efficient and located where the energy is needed. In this paper we focus on micro Combined Heat and Power generators. For such generators, the production capacity is determined and limited by the heat demand. To keep the global electricity network stable, information about the production capacity of the heat-driven generators is required in advance. In this paper we present methods to perform heat demand prediction of individual households based on neural network techniques. Using different input sets and a so called sliding window, the quality of the predictions can be improved significantly. Simulations show that these improvements have a positive impact on controlling the distributed microgenerators

Optimal Fully Electric Vehicle load balancing with an ADMM algorithm in Smartgrids

In this paper we present a system architecture and a suitable control methodology for the load balancing of Fully Electric Vehicles at Charging Station (CS). Within the proposed architecture, control methodologies allow to adapt Distributed Energy Resources (DER) generation profiles and active loads to ensure economic benefits to each actor. The key aspect is the organization in two levels of control: at local level a Load Area Controller (LAC) optimally calculates the FEVs charging sessions, while at higher level a Macro Load Area Aggregator (MLAA) provides DER with energy production profiles, and LACs with energy withdrawal profiles. Proposed control methodologies involve the solution of a Walrasian market equilibrium and the design of a distributed algorithm.Comment: This paper has been accepted for the 21st Mediterranean Conference on Control and Automation, therefore it is subjected to IEEE Copyrights. See IEEE copyright notice at http://www.ieee.org/documents/ieeecopyrightform.pd

Distributed multi-agent algorithm for residential energy management in smart grids

Distributed renewable power generators, such as solar cells and wind turbines are difficult to predict, making the demand-supply problem more complex than in the traditional energy production scenario. They also introduce bidirectional energy flows in the low-voltage power grid, possibly causing voltage violations and grid instabilities. In this article we describe a distributed algorithm for residential energy management in smart power grids. This algorithm consists of a market-oriented multi-agent system using virtual energy prices, levels of renewable energy in the real-time production mix, and historical price information, to achieve a shifting of loads to periods with a high production of renewable energy. Evaluations in our smart grid simulator for three scenarios show that the designed algorithm is capable of improving the self consumption of renewable energy in a residential area and reducing the average and peak loads for externally supplied power

Energy security issues in contemporary Europe

Throughout the history of mankind, energy security has been always seen as a means of protection from disruptions of essential energy systems. The idea of protection from disorders emerged from the process of securing political and military control over energy resources to set up policies and measures on managing risks that affect all elements of energy systems. The various systems placed in a place to achieve energy security are the driving force towards the energy innovations or emerging trends in the energy sector. Our paper discusses energy security status and innovations in the energy sector in European Union (EU). We analyze the recent up-to-date developments of the energy policy and exploitation of energy sources, as well as scrutinize the channels of energy streaming to the EU countries and the risks associated with this energy import. Moreover, we argue that the shift to the low-carbon production of energy and the massive deployment of renewable energy sources (RES) might become the key issue in ensuring the energy security and independency of the EU from its external energy supplies. Both RES, distributed energy resources (DER) and “green energy” that will be based on the energy efficiency and the shift to the alternative energy supply might change the energy security status quo for the EU

New hadrons as ultra-high energy cosmic rays

Ultra-high energy cosmic ray (UHECR) protons produced by uniformly distributed astrophysical sources contradict the energy spectrum measured by both the AGASA and HiRes experiments, assuming the small scale clustering of UHECR observed by AGASA is caused by point-like sources. In that case, the small number of sources leads to a sharp exponential cutoff at the energy E<10^{20} eV in the UHECR spectrum. New hadrons with mass 1.5-3 GeV can solve this cutoff problem. For the first time we discuss the production of such hadrons in proton collisions with infrared/optical photons in astrophysical sources. This production mechanism, in contrast to proton-proton collisions, requires the acceleration of protons only to energies E<10^{21} eV. The diffuse gamma-ray and neutrino fluxes in this model obey all existing experimental limits. We predict large UHE neutrino fluxes well above the sensitivity of the next generation of high-energy neutrino experiments. As an example we study hadrons containing a light bottom squark. These models can be tested by accelerator experiments, UHECR observatories and neutrino telescopes.Comment: 17 pages, revtex style; v2: shortened, as to appear in PR