Search

This paper presents an advanced market bidding and operation strategy for the joint participation of a solar plant with storage in Energy and secondary reserve markets (SRMs). A linear optimization is applied in order to calculate the optimal day-ahead and intraday market bids through a model predictive control (MPC) approach, considering solar generation forecast, hourly market prices as well as battery acquisition, operating and degradation costs. Moreover, several rule-based strategies are selected to participate in secondary reserve markets, focused on more profitable and reliable hours through the estimation of market prices and the expected operation of the battery. Finally, the real-time operation tackles all the uncertain parameters, such as the reserve requirements and solar forecast errors, with the objective of achieving an optimal tradeoff between market profits, battery costs and market technical fulfillments. Several market strategies have been compared in techno-economic terms with annual real market data. Regarding the most advanced strategy, market benefits are improved 6.42% from base case and non-fulfillment level of SRM is reduced up to 1.49%. [ABSTRACT FROM AUTHOR]

Renewable power plants must comply with certain codes and requirements to be connected to the grid, being the ramp-rate compliance one of the most challenging requirements, especially for photovoltaic or wind energy generation plants. Battery-based energy storage systems represent a promising solution due to the fast dynamics of electrochemical storage systems, besides their scalability and flexibility. However, large-scale battery energy storage systems are still too expensive to be a mass market solution for the renewable energy resources integration. Thus, in order to make battery investment economically viable, the use of second life batteries is investigated in the paper. This paper proposes a method to determine the optimal sizing of a second life battery energy storage system (SLBESS). SLBESS performance is also validated and, as an ultimate step, the power exchanged with the batteries is calculated during one-year operation. The power profile obtained is further used to define the cycling patterns for laboratory testing of second life batteries and to study their ageing evolution when used for the power smoothing renewable integration application. Real photovoltaic energy generation data from a Spanish plant were used for the study. [ABSTRACT FROM AUTHOR]

Energy storage systems (ESS) when integrated with large-scale photovoltaic (PV) plants, constituting a so-called Intelligent PV (IPV) power plant, are able to contribute to improve the economic viability of these power plants and to help them participate in the electricity markets as other traditional generators. In this paper, the sizing and control strategy co-optimization for an existing IPV power plant is proposed and implemented. A global linear programming (LP) optimization algorithm is developed, where the optimal components sizing is computed directly in the same optimization as the operating management of the storage system. In the IPV power plant design stage, the LP optimization is applied to obtain the optimal energy storage sizing parameters (capacity and power rate). In the operation stage, the same LP algorithm optimizes the control strategy to participate to electricity markets. This participation is based on the online model predictive control (MPC) to counteract PV forecast errors by participating in intraday markets. The proposed approach (optimal ESS sizing and online MPC) increases the daily economic benefits around 20%. [ABSTRACT FROM PUBLISHER]

Energy storage systems (ESS) integration with large-scale photovoltaic (PV) power plants, named intelligent PV (IPV) power plant, could contribute to improving the viability of these plants and to provide ancillary services to the main grid. The number and extension of the provided services depend not only on the size of the ESS, but also on the manner in which this storage system is managed. In this paper, the market participation based on different firming control strategies of an IPV power plant is proposed to optimize the economic exploitation based on the storage system management considering PV generation predictions. The most appropriate firming control strategy is selected to participate on the daily market, which is strengthened with an online model predictive control (MPC) to compensate the PV prediction errors participating in the intraday market. The real operation of the Iberian Peninsula market integration is also explained and analyzed. The development of this management strategy considers as a case study a real IPV plant located in Tudela (Navarre, Spain) and owned by Acciona Energía, which included a Lithium-ion based ESS from 2012 to 2013 in the framework of a European project. The results show that considering the MPC approach, an increase around 20% in the benefits is obtained by comparing with the daily market participation. [ABSTRACT FROM PUBLISHER]

In this paper, an optimal energy management strategy (EMS) for a light rail vehicle with an onboard energy storage system (ESS) combining batteries (BT) and supercapacitors (SC) is presented. The optimal operating targets for the proposed EMS and ESS sizing (BT+SC) are obtained by multiobjective (MO) optimization with genetic algorithms. The MO fitness function is expressed in economic terms and includes the cost of the energy absorbed from the catenary, as well as the operating cost of the BT and SC pack (initial investment and cycling cost). The case study selected is the tramway of Seville. The aim is to minimize the daily operating cost of the tramway, taking into account the fulfillment of the tramway performance in the catenary-less zone and the BT-SC degradation by cycling during operation. Furthermore, a sizing analysis is done by comparing the current scenario (SC-based) with the proposed approach (optimal EMS and sizing) in terms of daily operating cost reduction. [ABSTRACT FROM PUBLISHER]

Fast charging is an essential stakeholder concern for achieving a deeper penetration of Electric Vehicles (EVs), as optimizing the charging times of conventional vehicles is as yet a bottleneck to be solved. An important drawback of EV's fast charging lies in the degradation suffered by the Li-ion Batteries (LIBs) at high charging currents. A deep understanding of the how these fast-charging activities affect the LIBs' degradation is necessary in order to design appropriate fast charging stations and EV powertrains for different scenarios and contexts. In this regard, the present paper analyzes the effect of fast charging on Libs' degradation under operation profiles from real driving cycles. Specifically, Battery Electric Buses (BEBs) driving profiles from three demos in European Cities (Gothenburg, Osnabrück and Barcelona) have been used in this analysis. In order to deduce the best practices for the design of the charging stations, different sizes for the chargers have been simulated, focusing on the analysis of the LIB degradation under each situation. Besides, for the design of the EV powertrain, different LIB sizes and LIB chemistries (Lithium Nickel Manganese Cobalt-NMC, Lithium Iron Phosphate-LFP, and Lithium Titanate Oxide-LTO) have also been proposed and compared in terms of LIB degradation. The results demonstrated that LTO batteries exhibited the lowest degradation, with capacity fade values under 1.5%/year in the nominal scenario (nominal charger and LIB sizes). As long as a full charging is ensured, reducing the fast charger size has been found to be a cost-effective measure, as the LTO degradation can be reduced at least to 1.21%/year. In addition, increasing the battery (BT) size has also been found to be a cost-effective approach for LTO batteries. In this case, it was found that for a 66% increase in capacity, the degradation can be reduced at least to 0.74%/year (more than 50% reduction). The obtained conclusions are seen as useful for the design of charging stations and EV's BT systems that undergo fast charging. [ABSTRACT FROM AUTHOR]

The paper presents use case simulations of fleets of electric buses in two cities in Europe, one with a warm Mediterranean climate and the other with a Northern European (cool temperate) climate, to compare the different climatic effects of the thermal management strategy and charging management strategy. Two bus routes are selected in each city, and the effects of their speed, elevation, and passenger profiles on the energy and thermal management strategy of vehicles are evaluated. A multi-objective optimization technique, the improved Simple Optimization technique, and a "brute-force" Monte Carlo technique were employed to determine the optimal number of chargers and charging power to minimize the total cost of operation of the fleet and the impact on the grid, while ensuring that all the buses in the fleet are able to realize their trips throughout the day and keeping the battery SoC within the constraints designated by the manufacturer. A mix of four different types of buses with different battery capacities and electric motor specifications constitute the bus fleet, and the effects that they have on charging priority are evaluated. Finally, different energy management strategies, including economy (ECO) features, such as ECO-comfort, ECO-driving, and ECO-charging, and their effects on the overall optimization are investigated. The single bus results indicate that 12 m buses have a significant battery capacity, allowing for multiple trips within their designated routes, while 18 m buses only have the battery capacity to allow for one or two trips. The fleet results for Barcelona city indicate an energy requirement of 4.42 GWh per year for a fleet of 36 buses, while for Gothenburg, the energy requirement is 5 GWh per year for a fleet of 20 buses. The higher energy requirement in Gothenburg can be attributed to the higher average velocities of the bus routes in Gothenburg, compared to those of the bus routes in Barcelona city. However, applying ECO-features can reduce the energy consumption by 15% in Barcelona city and by 40% in Gothenburg. The significant reduction in Gothenburg is due to the more effective application of the ECO-driving and ECO-charging strategies. The application of ECO-charging also reduces the average grid load by more than 10%, while shifting the charging towards non-peak hours. Finally, the optimization process results in a reduction of the total fleet energy consumption of up to 30% in Barcelona city, while in Gothenburg, the total cost of ownership of the fleet is reduced by 9%. [ABSTRACT FROM AUTHOR]