Mehdi Rahmani-andebili

Price-Controlled Energy Management of Smart Homes for Maximizing Profit of a GENCO

In this paper, price-controlled energy management is investigated in a bi-level optimization framework, that is, energy scheduling problem of smart homes (SHs) and generation scheduling and unit commitment (UC) problems of a generation company (GENCO). SHs as the responsive customers (respect to the energy management) include a variety of sources such as photovoltaic (PV) panels, diesel generator, and battery as an energy storage. In addition, SHs are able to transact electricity with the GENCO through the power system. In this paper, the goal of GENCO is to design an optimal energy management scheme (optimal price of electricity) to maximize its daily profit. Herein, each SH reacts to the energy management scheme and reschedules its energy resources to minimize its daily operation cost. In this paper, a scenario-based stochastic optimization approach is applied in the energy scheduling problem of an SH to address the variability and uncertainty issues of the PV panels. Also, a combination of genetic algorithm (GA) and linear programming is applied as the optimization tool for the energy scheduling problem of an SH. Moreover, lambda-iteration economic dispatch and GA techniques are applied to solve the generation scheduling and UC problems of the GENCO, respectively. The numerical study demonstrates that in order to reach the maximum profit of GENCO, the energy management must be optimally designed and implemented; otherwise, the energy management scheme may result in detriment. Moreover, it is shown that each SH is able to get benefit from the energy management scheme and minimize its daily operation cost.

Energy Scheduling for a Smart Home Applying Stochastic Model Predictive Control

A smart home (SH) can have a variety of sources such as diesel generator (DG), photovoltaic (PV) panels installed on the roof of the SH, and plug-in electric vehicle (PEV) as an energy storage. In addition, a SH is able to transact power with the local distribution company (DISCO) through the grid. This study intends to investigate the energy scheduling problem of a SH so that the daily electricity consumption cost of the SH is minimized. Herein, the challenges include modeling the economic and technical constraints of the DG and the battery of the PEV, and also dealing with the variability and uncertainties concerned with the power of the PV panels that make the problem a nonlinear, dynamic (time-varying), and stochastic optimization problem. In order to handle the variability and uncertainties of the power of the PV panels, a stochastic model predictive control (MPC) is applied. Herein, a combination of genetic algorithm (GA) and linear programming (GA-LP) approach is applied as the optimization tool. The numerical study demonstrates the competence of the proposed approach for decreasing the operation cost.

Stochastic Optimization for Combined Economic and Emission Dispatch with Renewables

Environmental issues of thermal power plants and depletion of natural energy resources are the main motivations for applying renewable energy sources (RESs) in power system. Therefore, it is important to consider RESs when performing combined economic and emission dispatch (CEED). In this study, in the variability and uncertainties concerned with RESs and load demand are addressed with batteries installed in the power system as energy storage systems and stochastic optimization applied to solve the problem. A case study is presented to demonstrate the economic and environmental benefits achieved as a result.

Cooperative distributed energy scheduling for smart homes applying stochastic model predictive control

In this study, distributed energy resources scheduling problem of the set of smart homes (SHs) is investigated considering their cooperation with their neighbors and applying a stochastic model predictive control (MPC). Herein, every SH has a variety of sources and each SH is able to transact power with the local distribution company (DISCO) through the grid and with other connected SHs. The challenges of problem include modeling the technical and economic constraints of sources and dealing with the variability and uncertainties concerned with the power of photovoltaic (PV) panels that make the problem a mixed-integer nonlinear programming (MINLP), dynamic, and stochastic optimization problem. In order to handle the variability and uncertainties of problem, a stochastic MPC is applied. The numerical study demonstrates that cooperation of SHs in the energy scheduling problem has a high potential for minimizing operation cost of SHs.

Combined emission and economic dispatch incorporating demand side resources

In this paper, a unique formulation for combined emission and economic dispatch (CEED) incorporating demand side resources (CEED-DSRs) is presented. Herein, the target of a generation company is minimizing total cost including power generation cost, penalty cost due to carbon emissions, and cost of demand response program (DRP) implementation. In solving the CEED-DSRs problem, one of the voluntary and incentive-based programs of DRPs, that is, emergency demand response program (EDRP) is chosen, and then different strategies are defined for implementing EDRP. The strategies are prioritized based on minimum cost of the CEED-DSRs problem. Potential benefits of the proposed CEED-DSRs include reduced total cost of problem, diminished carbon emission level of thermal power plants, and improved system load factor.

Cooperative Distributed Energy Scheduling in Microgrids

This chapter introduces a multi-time scale model predictive control (MPC) approach which is stochastically applied in the cooperative distributed energy scheduling problem of the microgrids (MG). The cooperative distributed approach is preferred, since a centralized one is not applicable in a competitive power market environment because it requires all the data of all the MGs, which is impractical. In this chapter, in order to deal with the variability and uncertainties associated with output power of the renewable energy resources (RES) and load demand, stochastic MPC is applied in distributed energy scheduling problem of MGs. Additionally, considering multi-time scale approach in the stochastic MPC is capable of simultaneously having vast vision for the optimization time horizon and precise resolution for the problem variables. Herein, each MG with a different set of sources is able to transact power with the electricity market and the neighboring MGs. The numerical study demonstrates that cooperation of the MGs in the distributed energy scheduling problem is beneficial, and also the multi-time scale MPC is advantageous compared to the single-time scale MPC in both non-cooperative and cooperative distributed energy scheduling problems.

Energy management of end users modeling their reaction from a GENCO's point of view

In this study, price-controlled energy management problem of the end users are investigated in the generation scheduling and unit commitment problems of a generation company (GENCO) to minimize its overall cost. Herein, the reaction of end users with respect to the energy management schemes is modeled considering different mathematical behavioral models for the end users. It is shown that price-controlled energy management of end users has a considerable potential for minimizing the operation cost of a GENCO. In addition, it is proven that just an optimal scheme of energy management is able to result in the minimum value of operation cost for the GENCO.

Joint and selective periodic component carrier assignment for LTE-A

The bandwidth demand for mobile Internet access is significantly increased with the number of mobile users. Carrier aggregation has been proposed to answer this demand in mobile networks. In carrier aggregation, the best available one or more component carriers of each band are assigned to each user to provide efficient services. Several works have been reported in the literature on mandatory and periodic component carrier assignment methods. Although the former works, especially periodic component carrier assignment methods, have significantly improved the performance of LTE-A systems, many limitations still exist. One limitation of previous works is that data transfer is interrupted during periodic component carrier assignment operations thus, decrease the performance of the system. Therefore, in this paper, selective periodic component carrier assignment technique, which allows continuous data transfer during periodic carrier assignment operations, is proposed and followed by integration of selective technique into four component carrier assignment methods: Least Load, Least Load Rate, Random, and Channel Quality to observe the performance improvements. Results indicate that the proposed selective technique increases the throughput ratio up to 18% and decreases average delay up to 50%. Our analysis and proposed technique will assist service providers to build efficient periodic component carrier assignment methods to improve the performance of the system by reducing delay and increasing throughput ratio.

Canopying plug-in electric vehicles parking lots with photovoltaic panels

In order to mitigate the energy security and environmental issues concerned with fossil fuels, renewable energy resources such photovoltaic (PV) panels are suggested to be utilized in the electrical distribution systems. In this study, in order to minimize charging cost of the plug-in electric vehicles (PEVs), conventional grid-supplied PEVs parking lots are proposed to be canopied with PV panels, since parking lots occupy a considerable surface of a city. Also, the solar parking lots can cool down PEVs, charge their batteries, and even deliver excess energy to the grid. In this paper, value of the net benefit due to equipping a solar parking lot with PV panels are investigated over the life time of the PV panels with and without presence an energy storage. Herein, the cost and income terms include investment cost for purchasing the PV panels, investment cost for purchasing a battery, and benefit due to charging the PEVs with free energy generated by the PV panels. Moreover, the problem is simulated from different staring years considering the real prices of the PV panels and battery. In addition, the cost-to-benefit transition year is determined in every simulation.