Real-Time Pricing Strategy Based on the Stability of Smart Grid for Green Internet of Things

The ever increasing demand of energy efficiency and the strong awareness of environment have led to the enhanced interests in green Internet of things (IoTs). How to efficiently deliver power, especially, with the smart grid based on the stability of network becomes a challenge for green IoTs. Therefore, in this paper we present a novel real-time pricing strategy based on the network stability in the green IoTs enabled smart grid. Firstly, the outage is analyzed by considering the imbalance of power supply and demand as well as the load uncertainty. Secondly, the problem of power supply with multiple-retailers is formulated as a Stackelberg game, where the optimal price can be obtained with the maximal profit for retailers and users. Thirdly, the stability of price is analyzed under the constraints. In addition, simulation results show the efficiency of the proposed strategy

Demand response strategy applied to planning the operation of an air conditioning system: Application to a medical center

Large air conditioning systems, such as those used in shopping and health centers, typically demand high amounts of energy. Several air conditioning technologies and energy management strategies seek to minimize consumption to reduce billing expenses and improve system efficiency. This work proposes a demand response framework to plan the daily operation of an air conditioning system with the aim of minimizing the energy cost and guaranteeing thermal comfort. The framework includes an electrical-analogous thermal model, the formulation of the energy optimization problem with thermal and electrical constraints. The ISO 7730 standard is used to evaluate thermal comfort. The approach is applied to the air conditioning system of a radiotherapy and medical imaging center in Argentina. The optimization problem is solved through a genetic algorithm. To evaluate the strategy, two scenarios with different power demands are proposed: Case 1 (with demands lower than 300 kW) and Case 2 (with a peak demand greater than 300 kW). The results are compared with those obtained from an on-off strategy control with hysteresis. Penalties for large demands are avoided in Case 2, and therefore an economic saving of ≅ 16.8% is achieved. The thermal comfort is improved in both cases, with thermal cost reduction of 40.6% and 29.2% for Cases 1 and 2, respectively.Fil: Bragagnolo, Sergio Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnologica Nacional. Facultad Regional Cordoba. Centro de Investigacion Desarrollo y Transferencia de Ingenieria En Energia Electrica.; ArgentinaFil: Schierloh, R. M.. Universidad Tecnológica Nacional. Facultad Regional Paraná; ArgentinaFil: Vega, Jorge Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Vaschetti, Jorge Carlos. Universidad Tecnologica Nacional. Facultad Regional Cordoba. Centro de Investigacion Desarrollo y Transferencia de Ingenieria En Energia Electrica.; Argentin

Simultaneous load management strategy for electronic manufacturing facilities by using EPSO algorithm

Increased power demand has contributed to the power generation tension. Thus, there were critical needs for a better Price Based Program (PBP) policy for the consumers. In Peninsular Malaysia, through the development of a policy for the regulated market plan, the Enhanced Time of Use (ETOU) tariff was introduced by the utility to promote better price signals to the industrial consumers who contribute to the most massive energy consumption every year. However, fewer industrial consumers join the program due to a lack of Load Management (LM) knowledge while not confident in the price rate signal compared to the previous tariffs. Due to that reason, this study proposed simultaneous LM strategies for the selected power consumption profile in the electronic manufacturing facilities. Meanwhile, the Evolutionary Particle Swarm Optimization (EPSO) was adopted to search for the upright power consumption profiles of those average 11 locations of the manufacturing. The analysis of the results has compared to the baseline existing flat and Time of Use (TOU) tariffs. The results show an improvement in the energy consumption and maximum demand costs reduction of ~14-16% when load management was applied correctly. It is hoped that this study's results could help companies’ management of developing a strategic plan for the successful load management program

Uso eficiente del consumo de energía eléctrica residencial basado en el método Montecarlo

The regulation, control and optimization in residential workloads are the main issues that now need to be evaluated and taken into account in the decision making in the future growth of residential loads, since this represents not only the losses by amento appliances but also losses by heat. This paper proposes a procedure using the Monte Carlo model whereby optimization taking into account charges and energy production is taken as random variables, this paper intends to produce a probabilistic optimization model, which will be taking into account the costs of lost, which usually losses tend to decrease when the load tends to decrease, or be the opposite case to increase losses. Aim is to move the characteristic curve of load according to user's needs without disrupting its comforts, this necessity arises for the reason that there is no way of accumulating power and must be consumed at the moment that is being generated in real-time. For this purpose it is necessary to have a load curve as defined users through applications that they have to daily and he did it through surveys of residential load in different part of the city, once with tabulated data is defined higher demand schedules and equipment that are to be carried out which should be turned off and not given use in schedules defined to be able to move the loads.La regulación, control y optimización en las cargas residenciales son los principales temas que en la actualidad necesitan ser evaluadas y tomadas en cuenta para la tomas de decisiones en el futuro crecimiento de las cargas residenciales, ya que esto representa no solo las perdidas por amento de aparatos eléctricos sino también de pérdidas por calor. En este trabajo se propone un procedimiento mediante el método de Montecarlo por lo cual la optimización se toman en cuenta las cargas y la producción de energía se toma como variables aleatorias, en este trabajo se pretende realizar un modelo probabilístico de optimización, que será tomando en cuenta los costos de perdidas, que generalmente las perdidas suelen disminuir cuando la carga tiende a disminuir, o sea este el caso contrario al aumentar perdidas. Lo que se pretende es desplazar la curva característica de carga en función de las necesidades de usuario sin interrumpir sus comodidades, esta necesidad surge por el motivo de que no existe la manera de acumular la energía y tiene que consumirse en el momento que se está generando en tiempo real. Para ello es necesario tener un curva de carga ya definida de los usuarios por medio de los usos que tienen a diario y se lo consiguió mediante las encuestas de carga residencial en diferentes punto de la ciudad, una vez con los datos tabulados se define los horarios de mayor demanda y los equipos que se van a realizar el control los mismos que deberán ser apagados y no dados de uso en horarios definidos para poder desplazar las cargas

Optimized energy consumption model for smart home using improved differential evolution algorithm

Abstract: This paper proposes an improved enhanced differential evolution algorithm for implementing demand response between aggregator and consumer. The proposed algorithm utilizes a secondary population archive, which contains unfit solutions that are discarded by the primary archive of the earlier proposed enhanced differential evolution algorithm. The secondary archive initializes, mutates and recombines candidates in order to improve their fitness and then passes them back to the primary archive for possible selection. The capability of this proposed algorithm is confirmed by comparing its performance with three other wellperforming evolutionary algorithms: enhanced differential evolution, multiobjective evolutionary algorithm based on dominance and decomposition, and non-dominated sorting genetic algorithm III. This is achieved by testing the algorithms’ ability to optimize a multiobjective optimization problem representing a smart home with demand response aggregator. Shiftable and non-shiftable loads are considered for the smart home which model energy usage profile for a typical household in Johannesburg, South Africa. In this study, renewable sources include battery bank and rooftop photovoltaic panels. Simulation results show that the proposed algorithm is able to optimize energy usage by balancing load scheduling and contribution of renewable sources, while maximizing user comfort and minimizing peak-to-average ratio