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This paper utilizes the thermal and wind energies in power to gas (P2G) process. The purpose of the proposed P2G system is to reduce the operating costs and environmental pollutions. The wind energy is used to produce Hydrogen (H2) via water electrolyzer. The Carbon dioxide (CO2) of thermal energies is also captured by Carbon Capture and Storage (CCS). The achieved H2 and CO2 are combined to make Methane (CH4) through Methanation reaction. The unit commitment denotes the optimal operation schedule of thermal generating units at each hour. The proposed model also determines the optimal size of wind energy. In the case of any event or outage, the wind energy recovery is studied by application of hybrid battery-capacitor storage system. The achieved CH4 from wind energy is sold to make profit. The wind speed volatility is included in the model and handled by stochastic programming. The proposed P2G achieves several purposes including optimal wind turbine sizing, uncertainty management, CO2 decrease and operating cost reduction. It is demonstrated that the Carbon tax can reduce the CO2 pollutions up to 8%. The high-cost and high-polluting generating systems like oil fired steam turbines are only used at on-peak hours like 17–21. These units produce about 6% of total energy but releases 11% of total CO2. The optimal capacity of wind generating system is obtained equal to 107 MW and the profit of process is estimated about 2.383 million $ in year. [ABSTRACT FROM AUTHOR]

The home energy management is an efficient tool to manage energy in the buildings that organizes different technologies and mathematical techniques to minimize energy cost. Home energy management often utilizes renewable energy resources to supply load demand in the building. Current home energy management systems utilize one or several of the available hardware-software capacity resources to deal with energy consumption in the buildings. However, a comprehensive model including various hardware and software capacity resources may increase the flexibility of the model. In this regard, this paper studies an efficient paradigm for home energy management in the building connected to electric grid. The proposed model forms an energy hub including the hardware resources (i.e., vehicle-to-home, wind turbine, and diesel generator) and software tools (i.e., demand response program). All the capacity resources and grid power are optimally adjusted to minimize the daily operational cost of the building as well as improvement of resiliency and self-healing. Wind energy and load uncertainty are modeled through stochastic programming. The seasonal pattern is considered for loads, prices, and wind energy. Simulation results demonstrate that operating all capacity resources minimizes the daily operational cost. When the wind energy, demand response program, vehicle-to-home, and diesel generator are not utilized, the cost is increased by 900, 230, 84, and 322%, respectively. It is also confirmed that the building not only can operate when one of the components is not connected, but also it is able to supply the demand under off-grid operation. • Home energy management is modeled on building connected to grid. • Building is supported by hardware and software capacity resources. • Demand response program, diesel generator, and vehicle-to-home are operated. • Wind and load uncertainty are incorporated by stochastic programming. • Resiliency and self-healing are studied following failures and events. [ABSTRACT FROM AUTHOR]

This paper optimizes the operation of electric vehicles in small charging stations deployed in electrical distribution networks. The grid is equipped with small-scale charging stations rather than one large-scale charging station. In addition, photovoltaic solar panels are installed on the grid to gain renewable energy benefits. The electric vehicles operate on vehicle-to-grid mode. The charging and discharging behavior of all the electric vehicles on all buses is optimized by the given method. The presented strategy optimizes the electric vehicles operation to damp out the renewable energy intermittency and energy cost reduction at the same time. And, it minimizes the charging-discharging cycles of the vehicles batteries in order to avoid battery degradation. The proposed problem is modeled as nonlinear stochastic programming including uncertainty of solar energy and solved by GAMS software. The results demonstrate that the suggested method can properly charge and discharge the vehicle-to-grid system. The vehicles are often charged on off-peak low-cost time periods and they are discharged at on-peak high-cost time-intervals. The Intermittency of solar cells is dealt with the achieved charging-discharging pattern for vehicle-to-grid system and cost of consumed energy is minimized by energy shifting. The results validate that the presented strategy can efficaciously achieve all the intended purposes at the same time. • The distribution grid is installed with multiple small-scale charging stations. • The electric vehicles operate on vehicle to grid mode. • The grid is supported by photovoltaic solar panels with uncertain energy profile. • The charging-discharging operation of vehicle to grid is optimized. • The vehicle to grid deals with uncertainty, peak load, and energy cost. [ABSTRACT FROM AUTHOR]

• Energy storage system is planned to minimize losses in active distribution grid. • Network is installed with distributed energy resources. • The planning determines optimal sizing and siting of energy storage systems. • Optimal operation pattern for both active-reactive powers is determined. • AC power flow including reactive power is carried out. This paper deals with energy storage system (ESS) in active distribution networks. The purpose is to install ESSs on the grid to minimize network losses. The problem is expressed as an optimization programming to minimize annualized cost of losses and annualized investment cost of ESSs at the same time. The constraints of the programming are given as security constraints of the network and ESS operational constraints. The network is also equipped with distributed energy resource (DER) and its uncertainty is modeled and dealt by means of stochastic programming. Different DERs including diesel, wind, and solar resources are modeled and studied. The proposed nonlinear mixed integer stochastic programming is solved by particle swarm optimization (PSO). AC power flow is adopted to consider both active and reactive powers in the model. The ESSs are modeled including both active and reactive powers. The introduced planning finds optimal location, capacity, and power for ESSs. Furthermore, the charging-discharging regime for active power of ESSs and injection-absorption pattern for reactive power of ESSs are determined. The introduced methodology is successfully simulated on a typical distribution network. The simulation results confirm that the planned strategy properly installs ESSs on the grid and minimizes network losses. The results demonstrate that the ESSs decrease network losses about 22%. Finally, considering reactive power for ESSs results in about 24% cost reduction. [ABSTRACT FROM AUTHOR]

Abstract This paper presents an efficient framework for converting renewable energy to gas and reducing Carbon dioxide (CO 2) footprint at the same time. The problem is presented in two levels. The first level is a minimization programming that minimizes operational cost and CO 2 of generators. The CO 2 is forwarded to the second level. In the second level, Carbon Capture and Storage (CCS) is designed to capture CO 2. The CO 2 is combined with Hydrogen and makes Methane (CH 4). The required Hydrogen is obtained from water electrolyzer that is supplied by the solar system. The capacity and size of water electrolyzer, solar system, and CCS is designed by the planning in the second level while this programming maximizes profit from selling Methane. As a result, the first level presents minimization programming (i.e., minimizing cost and CO 2) and the second level presents maximization programming (i.e., maximizing profit). The programming is developed taking into account solar uncertainty. The stochastic programming is implemented to cope with uncertainties. The problem is formulated as binary mixed integer linear programming and solved by GAMS software. The proposed power to gas (P2G) procedure efficiently designs proper solar system and deals with intermittency of solar energy, reduces CO 2 footprint, maximizes profit, and minimizes operational cost of generators at the same time. Highlights • Operational cost and CO 2 is minimized for nonrenewable generators. • Considering Carbon tax operates low-cost and low-polluting units. • Optimal size of solar system is designed equal to 190 MW. • Annualized profit of power to gas system is 1.617 Million $ per year. • Carbon capture system captures 1621.5 (kg/day) of CO 2. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a unified model for home energy management. The proposed model optimizes the cogeneration of wind, solar, and battery storage units. The introduced tool considers electric and hydrogen vehicles and provides optimal charging pattern for them. The water electrolyze is also modeled to produced breathable oxygen and hydrogen from water. As well, the load modeling options such as adjustable and interruptible loads are included in the planning in order to increase the flexibility and adeptness of the proposed energy management system. The uncertainties of wind and solar powers are included resulting in a stochastic programming. The proposed stochastic optimization problem is mathematically expressed as a mixed integer linear programming and solved by GAMS software. The problem minimizes cost of energy consumption in the building subject to the operational constraints of wind unit, solar panel, battery system, loads, electric-hydrogen vehicles, and electricity grid. The proposed test building is studied under two states including connected to the electrical grid and disconnected from the gird (i.e., islanding mode or NetZero energy home). The impacts of the proposed planning on the environmental pollution are also considered and simulated. The results verify that the proposed strategy can successfully utilize wind-solar-battery units to supply the load, charging the electric-hydrogen vehicles, and reduction of the pollution. As well, it is demonstrated that the operation of the home under islanding mode is completely different from the connected state. Highlights • Hybrid wind-solar-battery is modeled and optimized. • Electric and hydrogen vehicles are included and optimized. • Adjustable and interruptible loads are considered and managed. • Uncertainty of wind-solar is included and stochastic problem is carried out. • The environmental pollution are studied and simulated. [ABSTRACT FROM AUTHOR]

This paper studies the effects of wind power on electricity market parameters. The penetration level of wind energy, location of wind turbines, and integration of multiple wind turbines are the main topics addressed in this paper. Flow-gate marginal pricing is the main electricity market concept which is analyzed and studied. Stochastic programming including wind power uncertainty is presented to minimize the annual operational cost of generators. The Flow-gate marginal pricing of transmission lines is also calculated. The Flow-gate marginal pricing is scrutinized under various wind power sizing, siting, and settings. The location, number, and capacity of wind turbines are changed to find out their impacts on the network Flow-gate marginal pricing. The proposed analyses and simulations are carried out using general algebraic modeling system software. [ABSTRACT FROM AUTHOR]

An optimal energy management is addressed in the residential building. The residential building is equipped with renewable energies including wind turbines and solar panels. The uncertainty of renewable energies is modeled by stochastic programming. The demand response program is simultaneously adopted to handle such uncertainty and reducing the energy cost. In this respect, four different loads are modeled in the building including interruptible, constant energy, constant power, and uninterruptible loads. The aforementioned loads are properly adjusted and dispatched for minimizing the energy cost as well as to deal with renewable energy intermittency. The bidirectional operation is modeled for the building and it can send energy to the grid or receive it from the upstream network. The results verify that the introduced model can efficiently harvest all possible energy of the wind-solar system, handle the uncertainty, minimize the cost, and operate as off-grid. All of these purposes are achieved by optimal dispatching and adjusting of the loads through the proposed demand response program. • Building is supported by coordination of various energy capacity resources. • Energy capacity resources are wind, solar, and demand response program. • Bidirectional and off-grid operations are admitted for building. • Demand response program includes different loading models and operations. • Uncertainty is incorporated by stochastic programming and demand response program. [ABSTRACT FROM AUTHOR]

Renewable energies and electrical loads usually show short-term variations in their energy profiles and they need to be precisely modeled in terms of time-scale and uncertainty. Correlation of time-scale, uncertainty, and simulation time must be studied to make an optimal tradeoff between these parameters. This paper aims to deal with this issue and it studies the correlation of time-scale and uncertainty in the renewable energy simulation. The different time scales including 15, 30, and 60 min are modeled and simulated. Uncertainty of electrical loads and wind energy are also incorporated. The introduced model is simulated and investigated on a typical building for energy management. Energy management tool is simulated under multiple time-scale patterns and wind-load uncertainty. The model is expressed as mixed integer stochastic programming and results confirm that considering shorter time-scale results in more precise outputs. It is demonstrated that 30, 15, and 5-min time-scale reduce the cost about 5, 3, and 0.8%, respectively. But they increase the simulation time about 100, 200, and 300%, respectively. As a result, 15-min time-scale is considered as the best case because it keeps both simulation time and model accuracy on the acceptable level. It is also shown that uncertainty in model increases the cost about 22% and reduces load by 10% and decreases the cost about 38%. • Correlation of time-scale modelling and uncertainty is simulation. • Multiple time-scales are modeled including 15, 30, and 60 min time-intervals. • Uncertainties of the loads and wind energy are incorporated. • Energy management is carried out to optimize energy consumption in building. • Seasonal profile is included for loads, wind energy, and electricity prices. [ABSTRACT FROM AUTHOR]