Applications of Probabilistic Forecasting in Smart Grids : A Review

This paper reviews the recent studies and works dealing with probabilistic forecasting models and their applications in smart grids. According to these studies, this paper tries to introduce a roadmap towards decision-making under uncertainty in a smart grid environment. In this way, it firstly discusses the common methods employed to predict the distribution of variables. Then, it reviews how the recent literature used these forecasting methods and for which uncertain parameters they wanted to obtain distributions. Unlike the existing reviews, this paper assesses several uncertain parameters for which probabilistic forecasting models have been developed. In the next stage, this paper provides an overview related to scenario generation of uncertain parameters using their distributions and how these scenarios are adopted for optimal decision-making. In this regard, this paper discusses three types of optimization problems aiming to capture uncertainties and reviews the related papers. Finally, we propose some future applications of probabilistic forecasting based on the flexibility challenges of power systems in the near future.© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Optimized Operation of Local Energy Community Providing Frequency Restoration Reserve

In order to unlock the maximum flexibility potential of all levels in the power system, distribution-network-located flexible energy resources (FERs) should play an important role in providing system-wide ancillary services. Frequency reserves are an example of system-wide ancillary services. In this regard, this paper deals with the optimal operation of a local energy community (LEC) located in the distribution network. The LEC is proposed to participate in providing manual frequency restoration reserves (mFRR) or tertiary reserves. In addition, the community is supposed to have a number of electric vehicles (EVs) and a battery energy storage system (BESS) as FERs. The scheduling of the community, which is fully compliant with the existing balancing market structure, comprises two stages. The first stage is performed in day-ahead, in which the energy community management center (ECMC) estimates the amount of available flexible capacities for mFRR provision. In this stage, control parameters are deployed by the ECMC in order to control the offered flexibility of the BESS. In the second stage, the real-time scheduling of the community is performed for each hour, taking into account the assigned and activated amount of reserve power. The target of the real-time stage is to maximize the community’s profit. Finally, the model is implemented utilizing a case study considering different day-ahead control parameters of the BESS. The results demonstrate that the proposed control parameters adopted in the day-ahead stage considerably affect the realtime profitability of the LEC. Moreover, according to the simulation results, participating in the mFRR market can bring additional profits for the LEC.© Firoozi, Hooman; Khajeh, Hosna; Laaksonen, Hannu. This work is licensed under a Creative Commons Attribution 4.0 License.fi=vertaisarvioitu|en=peerReviewed

Optimized operation of local energy community with flexible energy resources providing local and system-wide flexibility services for DSO and TSO needs

This paper proposes the participation of a local energy community (LEC) in providing flexibility services to electrical network. In this regard, the LEC is proposed to provide active power support for the distribution system operator (DSO) in order to control voltage and manage the congestion of the local LV feeders. The LEC also simultaneously provides frequency containment reserves for normal operation (FCR-N) to the transmission system operator (TSO) for system-wide flexibility needs. The proposed model is implemented for a hypothetical case study. The results demonstrate the high potential of the LEC as a flexibility services provider for both DSOs and TSOs.©2021 IET. This paper is a postprint of a paper submitted to and accepted for publication in CIRED 2021 - The 26th International Conference and Exhibition on Electricity Distribution and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.fi=vertaisarvioitu|en=peerReviewed

Increasing Self-Sufficiency of Energy Community by Common Thermal Energy Storage

Customers energy consumption pattern affects directly the grid burden, especially during peak hours. In recent years, many different control methods have been proposed to shift the energy consumption to off-peak hours through demand response (DR) management. In order to have effective DR energy management, optimization has a key role. Thus, increasing the benefits for the customers and encouraging them to consider the new controlling approaches in their daily energy consumption pattern is needed for increased customer participation. On the other hand, renewables are integrated with the buildings to decrease the buildings’ energy costs and dependency on the grid utilities. This study moves a step further and considers a few numbers of neighboring houses as an energy community. The community commits to sharing their produced energy from the individual distributed solar system with each other and increasing their energy self-sufficiency by minimizing the import and export of power from/to the grid. This research focuses on applying common electric heat energy storage when community’s own solar PV generation is used to thermal energy generation/storing in heat storage and compares it with the case in which each house has its own distributed thermal energy storage. Then, different sized thermal storages are tested for the community to find the best solution. The results are compared in terms of import and export of energy, annual costs and the payback-time. It is concluded that the community with common thermal energy storage could decrease the energy exchange with the grid and the payback-time of the investments could be reduced for the community members.©2022 the Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/fi=vertaisarvioitu|en=peerReviewed

Flexibility Potential of a Smart Home to Provide TSO-DSO-level Services

The high penetration of intermittent renewable-based power into modern power systems increases the need for more technical ancillary services from flexible energy resources. Smart homes could provide different flexibility services related to active power control services and therefore fulfill a part of the flexibility needs of system operators. In this regard, the estimation of the flexible capacities of each smart home's flexible device is of key importance. Correspondingly, this paper first estimates the flexible capacities of a smart home with controllable devices as flexible resources. The flexible capacity of each appliance is estimated considering its flexible and non-flexible operations. Besides, the local and system-wide flexibility services are introduced and the paper discusses whether a smart home can provide these types of services. In the simulations of this paper, the flexible capacity of each household appliance is estimated and compared to each other. Finally, the profitability of the smart home's battery energy storage multi-use is analyzed when it is providing three different types of flexibility services for the transmission system operator's needs. The results demonstrate that in some scenarios, the smart home's battery energy storage can increase its profits by providing transmission-system-level flexibility.© 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)This work was supported in part by the FLEXIMAR Project (novel marketplace for energy flexibility) through Business Finland under Grant 6988/31/2018, and in part by the Finnish companies.fi=vertaisarvioitu|en=peerReviewed

Optimized siting and sizing of distribution-network-connected battery energy storage system providing flexibility services for system operators

This paper develops a two-stage model to site and size a battery energy storage system in a distribution network. The purpose of the battery energy storage system is to provide local flexibility services for the distribution system operator and frequency containment reserve for normal operation (FCR-N) for the transmission system operator. In the first stage, the priority is to fulfil the flexibility needs of the distribution system operator by managing congestions or interruptions of supply in the local network. Thus, the first stage allocates the battery to ensure reliable electricity supply in the local distribution network. The minimum required size of the battery is also determined in the first stage. The second stage optimally sizes the battery energy storage system to boost the profit by providing frequency containment reserve for normal operation. The first and second stages both solve stochastic optimization problems to design the battery energy storage system. However, the first stage considers worst-case scenarios while the second stage utilizes the most probable scenarios derived from the historical data. To validate the proposed model, real-world data from the years 2021 and 2022 in Finland are employed. The battery placement is conducted for both the IEEE 33-bus system and a Finnish case study. The profitability of the model is compared across different cases for the Finnish case study. Finally, the paper assesses the impacts of cycle aging on the battery's total profit.© 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

A fuzzy logic control of a smart home with energy storage providing active and reactive power flexibility services

There is a need for enhanced flexibility to allow the high penetration of intermittent renewable power into the power system. In this way, transmission system operators (TSO) need more flexible energy resources that help to control the power system frequency by using balancing services. Distribution system operators (DSO) also seek new flexible energy resources that can counteract stochasticity, control voltage level, and manage congestions in distribution networks. Smart homes located in distribution networks are potential resources. Hence, this paper considers a smart home with flexible appliances and devices, including a battery energy storage system (BESS) interfaced with an inverter, an air conditioner (AC), and an electric vehicle (EV). The smart home aims to provide the system operators with coordinated frequency and DSO-level services while respecting the thermal comfort and schedules of the household residence. The inverter-interfaced BESS not only provides active power support for TSO and DSO, but it also injects and consumes reactive power if the DSO needs local flexibility. Fuzzy logic control system is deployed to obtain this goal. In the simulation section, a smart home with flexible appliances is scheduled. Different operations and the economic outcomes are discussed for the smart home considering real-world data.© 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Microgrids as energy and flexibility providers for TSO-level networks

In the future, the utilisation of technical services from distribution network connected flexible energy resources will be increasingly needed. Microgrid (MG) typically has multiple different flexible resources like distributed generation units, battery energy storages, electric vehicles and controllable loads. Therefore, MGs can be also seen as flexible resources from the power system point of view. This study will present a new method to adopt the flexibility of MGs for transmission network needs. Therefore, an MG aggregator is proposed to be responsible for scheduling several MGs with various resources aiming to participate in the transmission system operator (TSO)-level energy and flexibility markets. The results of the scheduling and different market participation of the MGs are simulated for the hypothetical MGs in Finland and the results will be discussed as well.© The Institution of Engineering and Technology 2021fi=vertaisarvioitu|en=peerReviewed

Towards flexibility trading at TSO-DSO-customer levels : a review

The serious problem of climate change has led the energy sector to modify its generation resources from fuel-based power plants to environmentally friendly renewable resources. However, these green resources are highly intermittent due to weather dependency and they produce increased risks of stability issues in power systems. The deployment of different flexible resources can help the system to become more resilient and secure against uncertainties caused by renewables. Flexible resources can be located at different levels in power systems like, for example, at the transmission-level (TSO), distribution-level (DSO) and customer-level. Each of these levels may have different structures of flexibility trading as well. This paper conducts a comprehensive review from the recent research related to flexible resources at various system levels in smart grids and assesses the trading structures of these resources. Finally, it analyzes the application of a newly emerged ICT technology, blockchain, in the context of flexibility trading.fi=vertaisarvioitu|en=peerReviewed

Comparison of optimized operation of energy community's flexibility considering different regulations and trading structures

This paper provides a comparison of an energy community energy trading under different regulations and trading structures. In this regard, it considers three cases. In the first case, the community is imposed a fixed price for consuming energy and the community manager aims to maximize the comfort level of its members. In the second case, the community pays for its consumption according to the market prices. The goal of the community manager is to minimize the total costs of the whole community. Finally, regarding the third case, the community trades power based on the negotiated prices with the same goal as the second case. The proposed models are implemented on a hypothetical community with 20 households as a member and the flexibilities of community's flexible energy resources are calculated for each case.©2021 IET. This paper is a postprint of a paper submitted to and accepted for publication in CIRED 2021 - The 26th International Conference and Exhibition on Electricity Distribution and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.fi=vertaisarvioitu|en=peerReviewed