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.

This study presents a model for the activities of the price‐maker microgrid aggregator (MGA). In this model, an MGA is considered to aggregate several microgrids (MGs) and be in charge of obtaining an optimal bidding strategy for MGs as well as scheduling their resources and demand. Two price‐maker strategies (the marginal and non‐marginal strategies for players) are proposed and the robust scheduling and optimal transactions of a price‐taker MGA are also obtained in order to analyse different bidding behaviour of MGA. A robust optimisation is used in this model in order to capture uncertainties associated with renewable generation in the worst‐case situation. Accordingly, the robust solution is obtained for the optimal scheduling of an MGA participating in the pool‐based day‐ahead electricity market. The proposed robust bidding strategies and scheduling of a price‐maker MGA are obtained considering a hypothetical test system and the results are compared with the bidding strategy and robust scheduling of a price‐taker MGA. The results show that the robust scheduling and also the market prices are completely changed for different strategies of the MGA. Also, using the proposed model for the price‐maker MGA increases the profits of MGs.

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.

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.

A large amount of renewable energy sources and electric vehicles will be integrated into future electricity distribution and transmission systems. New flexibility services from distribution network are needed to manage the related challenges. This paper proposes a local flexible capacity market (LFCM) in the distribution network providing system-wide and local flexibility services for transmission (TSO) and distribution system operators (DSO). The TSO and the DSO play the role of buyers, whereas prosumers connected to the distribution network are the sellers. The LFCM consists of three stages. At the first stage, the offers of flexibility sellers are matched with the bids of flexibility buyers aiming to maximize the social welfare of all participants. At the second stage, the accepted flexible capacities are checked by the DSO not to violate the constraints of the local network. The third stage accepts the offers of the sellers based on the results of the previous stage. The results related to the chosen case study demonstrate that the local flexible resources can help the DSO control the voltage and manage periods of congestion. Besides, the owners of the resources can obtain revenues by selling flexibility services while improving electricity supply reliability.

This paper presents a new holistic approach that combines solutions for the future power systems. It describes clearly how solar energy is definitely the best outlet for a clean and sustainable planet, either due to their use in both vertical (V) or horizontal (H) forms such as: hydroelectric V&H, wind V&H, thermo-oceanic V&H, water movement sea V&H (tides and waves), solar thermoelectric, PV, and surface geothermal energy. New points of view and simple formulas are suggested to calculate the best characteristic intensity, storage means and frequency for specific places and how to manage the most well-known renewable sources of energy. Future renewables-based power system requires a huge amount of flexibility from different type and size of controllable energy resources. These flexible energy resources can be used in an aggregated manner to provide different ancillary services for the distribution and transmission network. In addition, flexible energy resources and renewable generation can be utilized in different kinds of energy communities and smart cities to benefit all stakeholders and society at the same time with future-proof market structures, new business models and management schemes enabling increased utilization of flexible energy resources. Many of the flexible energy resources and renewable-based generation units are also inverter-interfaced and therefore the authors present future power converter systems for energy sources as well as the latest age of multilevel converters.