A Bi-Layer Multi-Objective Techno-Economical Optimization Model for Optimal Integration of Distributed Energy Resources into Smart/Micro Grids

The energy management system is executed in microgrids for optimal integration of distributed energy resources (DERs) into the power distribution grids. To this end, various strategies have been more focused on cost reduction, whereas effectively both economic and technical indices/factors have to be considered simultaneously. Therefore, in this paper, a two-layer optimization model is proposed to minimize the operation costs, voltage fluctuations, and power losses of smart microgrids. In the outer-layer, the size and capacity of DERs including renewable energy sources (RES), electric vehicles (EV) charging stations and energy storage systems (ESS), are obtained simultaneously. The inner-layer corresponds to the scheduled operation of EVs and ESSs using an integrated coordination model (ICM). The ICM is a fuzzy interface that has been adopted to address the multi-objectivity of the cost function developed based on hourly demand response, state of charges of EVs and ESS, and electricity price. Demand response is implemented in the ICM to investigate the effect of time-of-use electricity prices on optimal energy management. To solve the optimization problem and load-flow equations, hybrid genetic algorithm (GA)-particle swarm optimization (PSO) and backward-forward sweep algorithms are deployed, respectively. One-day simulation results confirm that the proposed model can reduce the power loss, voltage fluctuations and electricity supply cost by 51%, 40.77%, and 55.21%, respectively, which can considerably improve power system stability and energy efficiency.</jats:p

Addressing Wind Power Intermittency in the Ercot and SPP Regions

This Article explores efforts to address challenges involving wind power intermittency in two United States power regions: the South- west Power Pool (“SPP”) and the Electric Reliability Council of Texas (“ERCOT”). SPP and ERCOT are good case studies regarding these issues because each has among the strongest wind resources in the country, most of which are in isolated, sparsely populated areas and need long transmission lines to reach major load (electricity consumption) centers. Those circumstances increase the challenge of integrating intermittent wind generation into the electric system (grid)

Operating Hydrogen-Based Energy Storage Systems in Wind Farms for Smooth Power Injection: A Penalty Fees Aware Model Predictive Control

Smooth power injection is one of the possible services that modern wind farms could provide in the not-so-far future, for which energy storage is required. Indeed, this is one among the three possible operations identified by the International Energy Agency (IEA)-Hydrogen Implementing Agreement (HIA) within the Task 24 final report, that may promote their integration into the main grid, in particular when paired to hydrogen-based energy storages. In general, energy storage can mitigate the inherent unpredictability of wind generation, providing that they are deployed with appropriate control algorithms. On the contrary, in the case of no storage, wind farm operations would be strongly affected, as well as their economic performances since the penalty fees wind farm owners/operators incur in case of mismatches between the contracted power and that actually delivered. This paper proposes a Model Predictive Control (MPC) algorithm that operates a Hydrogen-based Energy Storage System (HESS), consisting of one electrolyzer, one fuel cell and one tank, paired to a wind farm committed to smooth power injection into the grid. The MPC relies on Mixed-Logic Dynamic (MLD) models of the electrolyzer and the fuel cell in order to leverage their advanced features and handles appropriate cost functions in order to account for the operating costs, the potential value of hydrogen as a fuel and the penalty fee mechanism that may negatively affect the expected profits generated by the injection of smooth power. Numerical simulations are conducted by considering wind generation profiles from a real wind farm in the center-south of Italy and spot prices according to the corresponding market zone. The results show the impact of each cost term on the performances of the controller and how they can be effectively combined in order to achieve some reasonable trade-off. In particular, it is highlighted that a static choice of the corresponding weights can lead to not very effective handling of the effects given by the combination of the system conditions with the various exogenous’, while a dynamic choice may suit the purpose instead. Moreover, the simulations show that the developed models and the set-up mathematical program can be fruitfully leveraged for inferring indications on the devices’ sizing.publishedVersio

The Incidence of Pollution Control Policies

This paper reviews theoretical and empirical literature on the household distribution of the costs and benefits of pollution control policies, and ways of integrating distributional issues into environmental cost/benefit analysis. Most studies find that policy costs fall disproportionately on poorer groups, though this is less pronounced when lifetime income is used, and policies affect prices of inputs used pervasively across the economy. The policy instrument itself is also critical; freely allocated emission permits may hurt the poor the most, as they transfer income to shareholders via scarcity rents created by higher prices, while emissions taxes offer opportunities for progressive revenue recycling. And although low-income households appear to bear a disproportionate share of environmental risks, policies that reduce risks are not always progressive, for example, they may alter property values in ways that benefit the wealthy. The review concludes by noting a number of areas where future research is badly needed.

The Incidence of Pollution Control Policies

This paper reviews theoretical and empirical literature on the household distribution of the costs and benefits of pollution control policies, and ways of integrating distributional issues into environmental cost–benefit analysis. Most studies find that policy costs fall disproportionately on poorer groups, though this is less pronounced when lifetime income is used, and policies affect prices of inputs used pervasively across the economy. The policy instrument itself is also critical; freely allocated emission permits may hurt the poor the most, as they transfer income to shareholders via scarcity rents created by higher prices, while emissions taxes offer opportunities for progressive revenue recycling. And although low-income households appear to bear a disproportionate share of environmental risks, policies that reduce risks are not always progressive, for example, they may alter property values in ways that benefit the wealthy. The review concludes by noting a number of areas where future research is badly needed.distributional incidence; emissions taxes; tradable permits; environmental benefits; distributional weights

Modelling the interaction between the energy system and road freight in Norway

By soft-linking models for transport demand, vehicle turnover and energy generation and use, we show how such models can complement each other and become more relevant and reliable policy support tools. A freight demand model is used to project commodity flows onto the 2050 horizon. An energy system model is used to map the relationships between energy prices, fiscal incentives, and optimal vehicle technologies. A stock-flow vehicle fleet model is used to calculate the time lag between innovation affecting new vehicles and the penetration of novel technology into the fleet. By running the latter two models in an iterative loop, we predict the flow of new vehicles with more or less decarbonized powertrains, contingent upon energy prices and fiscal incentives, while also obtaining a well-founded and more realistic assessment of the time needed for radical CO2 mitigation. The methodology is illustrated through a scenario developed for Norway.Modelling the interaction between the energy system and road freight in NorwaypublishedVersio

Integrated PHEV Charging Loads Forecasting Model and Optimization Strategies

In this dissertation, an integrated Plug-in Electric Vehicle (PHEV) charging loads forecasting model is developed for regular distribution level system and microgrid system. For regular distribution system, charging schedule optimization is followed up. The objectives are 1. Better cooperation with renewable energy sources (especially wind). 2. Relieving the pressure of current distribution transformers in condition of high penetration level PHEVs. As for microgrid, renewable energy power plants (wind, solar) plays a more important role than regular system. Due to the fluctuation of solar and wind plants\u27 output, an empirical probabilistic model is developed to predict their hourly output. On the other hand, PHEVs are not only considered at the charging loads, but also the discharging output via Vehicle to Grid (V2G) method which can greatly affect the economic dispatch for all the micro energy sources in microgrid. Optimization is performed for economic dispatch considering conventional, renewable power plants, and PHEVs. The simulation in both cases results reveal that there is a great potential for optimization of PHEVs\u27 charging schedule. Furthermore, PHEVs with V2G capability can be an indispensable supplement in modern microgrid