One of the solutions to cope with the increased need for flexibility in power systems is contract-based trading of power flexibility (FlexCon). Such contracts aim to overcome the uncertainties of renewable generation and variation of consumption. The effects of FlexCons on the existing market environment—especially on balancing markets (BLMs)—need to be explored further. Accordingly, this paper presents a methodology to optimize FlexCons and manual frequency restoration reserve–energy activation market (mFRR-EAM) considering the transmission system flexibility issues. FlexCons are between variable renewable energy producers (VREPs) and electricity retailers (RETs), and through this instrument, participants can exchange their deviation of production and consumption from their day-ahead market (DAM) schedule as a source of power flexibility to cope with the volatile market prices and assist with solving flexibility needs. In the proposed methodology, introduced entities as FlexCon operators clear FlexCons in different locations of the system and send the results to FlexCon participants and the transmission system operator (TSO). Subsequently, the TSO clears the mFRR-EAM close to the real-time power delivery to address the remaining flexibility requirements. The impacts of considering FlexCons along with the mFRR-EAM on operational and economic aspects of the social welfare maximization problem of mFRR-EAM and optimal power flow outcomes are assessed. A three-bus illustrative example and a modified IEEE 30-bus system are used as case studies. The results indicate that when considering FlexCons in the system, a lower flexibility price and lower operation cost in the mFRR-EAM can be achieved. Moreover, the presence of FlexCons along with the mFRR-EAM contributes to lower congestion in transmission lines.

Investment in renewable energy generation is integral to transitioning to sustainable power and energy systems. In this regard, the concept of hosting capacity (HC) is a useful tool for renewable generation investors and system operators to identify the maximum quantity of connected renewable resources without modification or strengthening of the grid. However, a considerable part of the extant research addresses the technical requirements of the problem in distribution systems while neglecting the transmission system and market constraints. Renewable generation uptake has reduced reliance on fossil fuel-based resources in the power sector, while also demonstrating capability to address the flexibility needs of the system. This paper proposes a market-based approach for maximizing renewable generation HC in transmission systems considering both energy and flexibility markets. To this end, a bilevel optimization problem is developed to study the profitability of maximizing renewable generation HC. In the upper-level problem, an HC maximization is developed with respect to the non-negative profitability of the new generation investment. The lower-level problem addresses social welfare maximization of energy and flexibility markets in which new renewable energy generation can participate. The formulations are transferred into a single-level mixed-integer linear programming (MILP) problem to avoid the nonlinearity of the bilevel model. The proposed model is applied to a 2-bus illustrative example and the IEEE 24-bus reliability test system (RTS). The results demonstrate that renewable generation units can improve their profitability by participating in the flexibility market and thereby increase the renewable generation HC from a market perspective.

Incorporating new business and trading models in the process of hosting capacity (HC) analyses of distribution systems is of great importance. Accordingly, this paper presents a two-stage methodology to investigate the effects of flexibility contracts (FlexCons) on the HC assessment of distribution grids. In the first stage, the maximization of FlexCon exchanges between photovoltaic (PV) solar owners and demands is addressed. In the second stage, an optimization-based HC calculation is carried out for various cases with and without considering FlexCons. In particular, the impacts of location and number of FlexCons on the HC are studied. The results of simulations on a 33-bus radial distribution network indicate an increase in PV-HC by considering contract-based trading of power flexibility through FlexCons.

Alternative solutions, instead of building new transmission lines, are needed to enable the fast electrification of sectors such as industry, transportation, and heating. This includes smart-grid technology allowing for an increase in hosting capacity without the need for building new transmission lines. The increase in hosting capacity beyond the firm hosting capacity is, with many of the schemes, related to the ability of the installation to tolerate curtailment. The paper gives an overview of different smart-grid schemes and how they potentially increase the hosting capacity. The connection of a large electrolyser installation for hydrogen production is used as an illustrative example introducing some of the issues.

Low or even zero carbon dioxide emissions will be an essential requirement for energy supplies in the near future. Besides transport and electricity generation, industry is another large carbon emitter. Hydrogen produced by renewable energy provides a flexible way of utilizing that energy. Hydrogen, as an energy carrier, could be stored in a large capacity compared to electricity. In Sweden, hydrogen will be used to replace coal for steel production. This paper discusses how the need for electricity to produce hydrogen will affect the electricity supply and power flow in the Swedish power grid, and whether it will result in increased emissions in other regions. Data of the Swedish system will be used to study the feasibility of implementing the hydrogen system from the power system viewpoint, and discuss the electricity price and emission issues caused by the hydrogen production in different scenarios. This paper concludes that the Swedish power grid is feasible for accommodating the additional electricity capacity requirement of producing green hydrogen for the steel industry. The obtained results could be references for decision makers, investors, and power system operators.

This paper surveys the most recent local flexibility markets (LFMs) initiatives in three Nordic countries: Finland, Norway, and Sweden. First, the significant distinctions in flexibility needs in the region are identified and analyzed. Then, considering the recently practiced LFMs in these countries, a review of their aims, description, and implementation is performed. Moreover, their key findings and future research directions are discussed. The LFM platforms evaluated in this review are EU-SysFlex, INTERRFACE, NODES, DRES2Market, CoordiNet, SthlmFlex, and InterFlex. The analyses assert that the new LFM models express a promising technical and economic vision for increasing flexibility from the costumers' and aggregators' side by delivering grid and system services for both transmission system operators (TSOs) and distribution system operators (DSOs) in a coordinated context.

The accuracy of models to capture the uncertainty of renewables significantly affects the planning and operation of renewable energy-based stand-alone (REB-SA) microgrids. This paper aims to first study different stochastic and deterministic models for renewables, then evaluate the performance of an REB-SA microgrid planning problem and provide qualitative and quantitative comparisons. A modified Metropolis-coupled Markov chain Monte Carlo simulation is considered for the first time in the planning of an REB-SA microgrid to predict the behavior of renewables with minimum iterations. The modified model is benchmarked against two prevalent models including the retrospective model with worst-case scenarios and the Monte Carlo simulation. The operations of three designed microgrids (by these three methods) are evaluated using the last three-year historical data of a city in northern Sweden including solar radiation, wind speed, the water flow of a river, and load consumption. The impacts of the considered methods on using PV panels and hydrogen systems are investigated. The results verify that the modified model decreases the risk of planning and operation of an REB-SA microgrid from the energy and power shortage viewpoints. Moreover, the designed microgrid with the modified model can cope with all possible scenarios from economic, technical, and environmental viewpoints.

Due to the lack of available flexibility sources to cope with different uncertainties in the real-time operation of stand-alone renewable energy-based microgrids, the stochastic behavior of uncertainty sources needs to be included in the planning stage. Since there is a high association between some of the uncertainty sources, defining a proper time series to represent the behavior of each source of uncertainty is a challenging issue. Consequently, uncertainty sources should be modeled in such a way that the designed microgrid be able to cope with all scenarios from probability and impact viewpoints. This paper proposes a modified Metropolis–coupled Markov chain Monte Carlo (MC)³ simulation to predict the stochastic behavior of different uncertainty sources in the planning of a stand-alone renewable energy-based microgrid. Solar radiation, wind speed, the water flow of a river, load consumption, and electricity price have been considered as primary sources of uncertainty. A novel data classification method is introduced within the (MC)³ simulation to model the time-dependency and the association between different uncertainty sources. Moreover, a novel curve-fitting approach is proposed to improve the accuracy of representing the multimodal distribution functions, modeling the Markov chain states, and the long-term probability of uncertainty sources. The predicted representative time series with the proposed modified (MC)³ model is benchmarked against the retrospective model, the long-term historical data, and the simple Monte Carlo simulation model to capture the stochastic behavior of uncertainty sources. The results show that the proposed model represents the probability distribution function of each source of uncertainty, the continuity of samples, time dependency, the association between different uncertainty sources, short-term and long-term trends, and the seasonality of uncertainty sources. Finally, results confirm that the proposed modified (MC)³ can appropriately predict all scenarios with high probability and impact.

Swedish government’s target is to have 100 per cent renewable electricity production by 2040. Currently, hydropower contributes the majority of renewable electricity generation of the country. The wind power capacity has increased significantly in the past decade. In this paper, practical data is used to study the possibility of reaching the 100% renewable electricity generation goal by replacing existing thermal generations with wind power generations. It is found that the Swedish electricity generation system can reach 100% renewable by tripling the existing wind power capacity combined with the existing hydropower in the country. Based on current growth rate of wind power installation, the goal could be reached within 20 years. Hourly simulation shows that 100 % renewable energy generation system composed by wind power and hydropower satisfy hourly operation requirements.