Two-stage Robust-Stochastic Electricity Market Clearing Considering Mobile Energy Storage in Rail transportation

This paper proposes a two-stage robust-stochastic framework to evaluate the effect of the battery-based energy storage transport (BEST) system in a day-ahead market-clearing model. The model integrates the energy market-clearing process with a train routing problem, where a time-space network is used to describe the limitations of the rail transport network (RTN). Likewise, a price-sensitive shiftable (PSS) demand bidding approach is applied to increase the flexibility of the power grid operation and reduce carbon emissions in the system. The main objective of the proposed model is to determine the optimal hourly location, charge/discharge scheduling of the BEST system, power dispatch of thermal units, flexible loads scheduling as well as finding the locational marginal price (LMP) considering the daily carbon emission limit of thermal units. The proposed two-stage framework allows the market operator to differentiate between the risk level of all existing uncertainties and achieve a more flexible decision-making model. The operator can modify the conservatism degree of the market-clearing using a non-probabilistic method based on info-gap decision theory (IGDT), to reduce the effect of wind power fluctuations in real-time. In contrast, a risk-neutral-based stochastic technique is used to meet power demand uncertainty. The results of the proposed mixed-integer linear programming (MILP) problem, confirm the potential of BEST and PSS demand in decreasing the LMP, line congestion, carbon emission, and daily operation cost

Review of trends and targets of complex systems for power system optimization

Optimization systems (OSs) allow operators of electrical power systems (PS) to optimally operate PSs and to also create optimal PS development plans. The inclusion of OSs in the PS is a big trend nowadays, and the demand for PS optimization tools and PS-OSs experts is growing. The aim of this review is to define the current dynamics and trends in PS optimization research and to present several papers that clearly and comprehensively describe PS OSs with characteristics corresponding to the identified current main trends in this research area. The current dynamics and trends of the research area were defined on the basis of the results of an analysis of the database of 255 PS-OS-presenting papers published from December 2015 to July 2019. Eleven main characteristics of the current PS OSs were identified. The results of the statistical analyses give four characteristics of PS OSs which are currently the most frequently presented in research papers: OSs for minimizing the price of electricity/OSs reducing PS operation costs, OSs for optimizing the operation of renewable energy sources, OSs for regulating the power consumption during the optimization process, and OSs for regulating the energy storage systems operation during the optimization process. Finally, individual identified characteristics of the current PS OSs are briefly described. In the analysis, all PS OSs presented in the observed time period were analyzed regardless of the part of the PS for which the operation was optimized by the PS OS, the voltage level of the optimized PS part, or the optimization goal of the PS OS.Web of Science135art. no. 107

Optimization-Based Market-Clearing Procedure with EVs Aggregator Participation

For the upcoming new generation of electric power systems, i.e. smart grids, one of the most important challenges is to achieve an adequate economic and technical management involving the different agents in the process. In order to deliver the available power from suppliers to consumers, a market-clearing mechanism is needed. At the same time, technical operation calls for controlling that technical limits are not reached to preserve the security of the system. In this environment, Electric Vehicles (EVs) are gaining importance both in economic and technical issues. In this paper, an optimization-based approach is proposed for clearing the market in a smart grid. The traditional participants in energy markets are included in the formulation, stressing the role of EVs aggregators. The results presented in this paper illustrate the influence of EVs in the market-clearing procedure. The benefits for the system and EVs aggregators are also studiedUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Minimizing wind power curtailment using a continuous-time risk-based model of generating units and bulk energy storage

Wind power curtailment (WPC) occurs because of the non-correlation between wind power generation (WPG) and load, and also due to the fast sub-hourly variations of WPG. Recently, advances in energy storage technologies facilitate the use of bulk energy storage units (ESUs) to provide the ramping required to respond to fast sub-hourly variations of WPGs. To minimize the sub-hourly WPC probability, this paper addresses a generic continuous-time risk-based model for sub-hourly scheduling of energy generating units and bulk ESUs in the day-ahead unit commitment (UC) problem. Accordingly, the Bernstein polynomials are hosted to model the continuous-time risk-based UC problem with ESU constraints. Also, the proposed continuous-time risk-based model ensures that the generating units and ESUs track the sub-hourly variations of WPG, while the load and generation are balanced in each sub-hourly intervals. Finally, the performance of the proposed model is demonstrated by simulating the IEEE 24-bus Reliability and Modified IEEE 118-bus test systems.©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Can electric vehicle charging be carbon neutral? Uniting smart charging and renewables

Growing numbers of plug-in electric vehicles in Europe will have an increasing impact on the electricity system. Using the agent-based simulation model PowerACE for ten electricity markets in Central Europe, we analyze how different charging strategies impact price levels and production- as well as consumption-based carbon emissions in France and Germany. The applied smart charging strategies consider spot market prices and/or real-time production from renewable energy sources. While total European carbon emissions do not change significantly in response to the charging strategy due to the comparatively small energy consumption of the electric vehicle fleet, our results show that all smart charging strategies reduce price levels on the spot market and lower total curtailment of renewables. Here, charging processes optimized according to hourly prices have the strongest effect. Furthermore, smart charging strategies reduce electricity purchasing costs for aggregators by about 10% compared to uncontrolled charging. In addition, the strategies allow aggregators to communicate near-zero allocated emissions for charging vehicles. An aggregator’s charging strategy expanding classic electricity cost minimization by limiting total national PEV demand to 10% of available electricity production from renewable energy sources leads to the most favorable results in both metrics, purchasing costs and allocated emissions. Finally, aggregators and plug-in electric vehicle owners would benefit from the availability of national, real-time Guarantees of Origin and the respective scarcity signals for renewable production

Optimal Charging Scheduling of Electric Vehicles in Smart Grids by Heuristic Algorithms

Transportation electrification has become an important issue in recent decades and the large scale deployment of electric vehicles (EVs) has yet to be achieved. The smart coordination of EV demand addresses an improvement in the flexibility of power systems and reduces the costs of power system investment. The uncertainty in EV drivers' behaviour is one of the main problems to solve to obtain an optimal integration of EVs into power systems. In this paper, an optimisation algorithm to coordinate the charging of EVs has been developed and implemented using a Genetic Algorithm (GA), where thermal line limits, the load on transformers, voltage limits and parking availability patterns are taken into account to establish an optimal load pattern for EV charging-based reliability. This methodology has been applied to an existing residential low-voltage system. The results indicate that a smart charging schedule for EVs leads to a flattening of the load profile, peak load shaving and the prevention of the aging of power system elements.This work has been partially funded by the Spanish Ministry of Industry, Energy and Tourism under contract DOMOCELL TSI-020100-2009-849