Optimized Charging of PV-Batteries for Households Using Real-Time Pricing Scheme: A Model and Heuristics-Based Implementation

Basmadjian, Robert

doi:10.3390/electronics9010113

Cited by 13 publications

(9 citation statements)

References 21 publications

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“…2222, which allows distributed and aggregated resources to participate in the electricity market starting at a capacity of 1 kW. The Texan market operator ERCOT already allows time-of-use tariffs, critical peak pricing, or peak time rebates (Du et al 2019); the first real-time pricing tariff has recently been launched in Germany by the company aWATTar (Basmadjian 2020).…”

Section: The Next Steps For Decentralized Energy Marketsmentioning

confidence: 99%

Engineering Energy Markets: The Past, the Present, and the Future

et al. 2021

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Since the beginning of the energy sector liberalization, the design of energy markets has become a prominent field of research. Markets nowadays facilitate efficient resource allocation in many fields of energy system operation, such as plant dispatch, control reserve provisioning, delimitation of related carbon emissions, grid congestion management, and, more recently, smart grid concepts and local energy trading. Therefore, good market designs play an important role in enabling the energy transition toward a more sustainable energy supply for all. In this chapter, we retrace how market engineering shaped the development of energy markets and how the research focus shifted from national wholesale markets to more decentralized and location-sensitive concepts.

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Section: The Next Steps For Decentralized Energy Marketsmentioning

confidence: 99%

Engineering Energy Markets: The Past, the Present, and the Future

et al. 2021

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“…As the penetration rate of renewable energy access is getting higher and higher, improving the carrying capacity of renewable energy is very important for the economic operation of the distribution network [22][23][24]. This paper uses coordinated control between second-use energy storage and photovoltaic power generation in order to achieve optimal dispatch operation of the distribution network.…”

Section: Multi-objective Optimal Dispatching Modelmentioning

confidence: 99%

Distribution System Renewable Hosting Capacity Maximization with Second-Use Electric Vehicle Storage Using Critical Capacity Retention Calculation Model

Jiang¹,

Li²,

Zhao

et al. 2020

Electronics

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The continuous increase of the penetration of distributed generation in the distribution network poses more severe challenges for its full accommodation. In this context, an energy storage system can be installed to enhance the ability to accommodate renewable energy because of its high flexibility. However, the investment cost of brand new energy storage equipment is so high that the widespread application of storage system in the distribution system has been limited. This article proposes a method, which aimed at optimizing energy storage dispatching in a distribution network, and takes the use of second-use electric vehicles (EV) batteries into account. A calculation model of power battery second-use capacity was established, the upper and lower bounds of the initial capacity of second-use energy storage system (SUESS) can be determined after the reorganization of the retired batteries, and then a multi-objective optimal dispatching model for the distribution system with SUESS can be established with reference to the capacity attenuation model and the daily mileage statistics model of the electric vehicle. The case study results show that configured SUESS in the distribution network can not only meet the needs of the distribution network to fully accommodate distributed generation, but also delay the upgrade of the distribution network.

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“…Furthermore, surplus PV generation does not always match with local Household (HH) load demand, hence battery Energy Storage Systems (ESS) and flexible loads, such as plugin Electric Vehicles (EVs), come into play. Managed by an Energy Management System (EMS), the ESS and flexible loads can be utilized for self-consumption, autarchy or cost optimization [6,11,19,36], peak shaving [12], load shifting [48], or local voltage control [2,49].…”

Section: Introductionmentioning

confidence: 99%

“…However, most of them do not forecast on household level. Proposed EMS controllers only aim at local ESS optimization [6,7] or cost-optimal EV charging [9] and, thus, do not exploit the flexibility potential on a global level. Solutions targeting flexibility provision typically do not address data privacy or uncertainty of flexibility delivery [4, 33-37, 40, 42].…”

Section: Introductionmentioning

confidence: 99%

Flexibility Disaggregation under Forecast Conditions

Danner

Seidemann

Lechl

et al. 2021

Proceedings of the Twelfth ACM International Conference on Future Energy Systems

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Stationary battery energy storage systems and electric vehicles become more and more popular at households with local photovoltaic generation. Besides improving self-consumption and autarchy, these batteries can provide flexibility to an external utility. Thereby, generation and demand uncertainty, as well as cost optimality, need to be considered when utilizing distributed flexibility.This paper discusses long short-term memory neural networks for photovoltaic generation forecast and persistence models for household load forecast with respect to their applicability in local energy management system optimization. Furthermore, a mixedinteger linear program is proposed to optimally utilize local flexible loads and storage systems. Its solution space yields the flexibility potential, which can be aggregated at flexibility pools. In order to disaggregate flexibility requests to a pool of distributed energy management systems, we propose a heuristic algorithm that can among others minimize the overall flexibility cost or maximize probability of flexibility delivery. The forecast models, the mixed integer linear program and the flexibility disaggregation are evaluated on realistic household photovoltaic and load profiles to demonstrate the full chain from local forecast to flexibility disaggregation under forecast conditions. Our experiments with flexibility disaggregation show that the probability to provide flexibility should not be neglected when it comes to distributed energy management optimization based on forecast models. CCS CONCEPTS• Hardware → Smart grid; • Computing methodologies → Uncertainty quantification; Neural networks.

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Optimized Charging of PV-Batteries for Households Using Real-Time Pricing Scheme: A Model and Heuristics-Based Implementation

Cited by 13 publications

References 21 publications

Engineering Energy Markets: The Past, the Present, and the Future

Engineering Energy Markets: The Past, the Present, and the Future

Distribution System Renewable Hosting Capacity Maximization with Second-Use Electric Vehicle Storage Using Critical Capacity Retention Calculation Model

Flexibility Disaggregation under Forecast Conditions

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