AC-feasible Local Flexibility Market with Continuous Trading

This paper proposes a novel continuous Local Flexibility Market where active power flexibility located in the distribution system can be traded. The market design engages the Market Operator, the Distribution System Operator and Market Participants with dispatchable assets. The proposed market operates in a single distribution system and considers network constraints via AC network sensitivities, calculated at an initial network operating point. Trading is possible when AC network constraints are respected and when anticipated network violations are alleviated or resolved. The implementation allows for partial bid matching and is computationally light, therefore, suitable for continuous trading applications. The proposed design is thoroughly described and is demonstrated in a test distribution system. It is shown that active power trading in the proposed market design can lead to resolution of line overloads.Comment: In proceedings of the 11th Bulk Power Systems Dynamics and Control Symposium (IREP 2022), July 25-30, 2022, Banff, Canad

Capacity Mechanisms in Europe and the US: A Comparative Analysis and a Real-Life Application for Greece

This paper presents a comparative analysis of various capacity mechanisms that are either in force or under approval in key countries/regions in Europe and the US. A detailed analysis on the necessities that led to the establishment of the capacity mechanisms, along with various fundamental technical and operational features associated with the design and operation of different capacity mechanisms, mainly in Europe (Italy, France, Germany, Belgium, Poland, Great Britain, Ireland, Cyprus) and complementarily in the US (PJM, New England), are presented. This analysis is complemented by a real-life application regarding the long-term capacity remuneration mechanism that is expected to be established in Greece in the near future. A detailed simulation of the envisaged capacity mechanism auctions under differentiated scenarios has been performed, regarding the future Greek power system operating conditions during the forthcoming decade (2022–2031). Test results illustrate that the outcome of the auctions is heavily dependent on the future energy generation mix and the market participants’ bidding strategy. Whereas, the total cost that will have to be undertaken by the electricity supply companies and, ultimately, by the end-consumers for the financing of the proposed capacity mechanism lies in the range of 5.5–8.7 €/MWh for the entire study period

Manual Frequency Restoration Reserve Activation Clearing Model

The integration of the European markets has started with the successful coupling of spot markets (day-ahead and intra-day) and is expected to continue with the coupling of balancing markets. In this paper, the optimization model for the activation of manual frequency restoration reserve (mFRR) is presented. The model incorporates all order types agreed among the European transmission system operators (TSOs) to be included in the Manually Activated Reserves Initiative (MARI) project. Additionally, the model incorporates the buying curve (demand) of mFRR with the possible tolerance band defined by the TSOs, order clearing constraints and the cross-zonal capacity (CZC) constraints, forming a mixed integer linear programming model. The methodology employs two distinct steps: In the first step, an order conversion process is employed for the markets applying the central-scheduling scheme, and in the second step, the mFRR activation process is executed by solving the presented model. The whole process is tested using a case, including twenty-five European control areas. The attained clearing results indicate that price convergence is achieved among the involved control areas, along with a reduction in the overall balancing costs mainly due to the imbalance netting that is implicitly performed during the joint mFRR balancing energy (BE) clearing process and due to the cross-border exchange of mFRR BE

Stochastic and Deterministic Unit Commitment Considering Uncertainty and Variability Reserves for High Renewable Integration

The uncertain and variable nature of renewable energy sources in modern power systems raises significant challenges in achieving the dual objective of reliable and economically efficient system operation. To address these challenges, advanced scheduling strategies have evolved during the past years, including the co-optimization of energy and reserves under deterministic or stochastic Unit Commitment (UC) modeling frameworks. This paper presents different deterministic and stochastic day-ahead UC formulations, with focus on the determination, allocation and deployment of reserves. An explicit distinction is proposed between the uncertainty and the variability reserve, capturing the twofold nature of renewable generation. The concept of multi-timing scheduling is proposed and applied in all UC policies, which allows for the optimal procurement of such reserves based on intra-hourly (real-time) intervals, when concurrently optimizing energy and commitments over hourly intervals. The day-ahead scheduling results are tested against different real-time dispatch regimes, with none or limited look-ahead capability, or with the use of the variability reserve, utilizing a modified version of the Greek power system. The results demonstrate the enhanced reliability achieved by applying the multi-timing scheduling concept and explicitly considering the variability reserve, and certain features regarding the allocation and deployment of reserves are discussed

Overview of Natural Gas Boiler Optimization Technologies and Potential Applications on Gas Load Balancing Services

Natural gas is a fossil fuel that has been widely used for various purposes, including residential and industrial applications. The combustion of natural gas, despite being more environmentally friendly than other fossil fuels such as petroleum, yields significant amounts of greenhouse gas emissions. Therefore, the optimization of natural gas consumption is a vital process in order to ensure that emission targets are met worldwide. Regarding residential consumption, advancements in terms of boiler technology, such as the usage of condensing boilers, have played a significant role in moving towards this direction. On top of that, the emergence of technologies such as smart homes, Internet of Things, and artificial intelligence provides opportunities for the development of automated optimization solutions, which can utilize data acquired from the boiler and various sensors in real-time, implement consumption forecasting methodologies, and accordingly provide control instructions in order to ensure optimal boiler functionality. Apart from energy consumption minimization, manual and automated optimization solutions can be utilized for balancing purposes, including natural gas demand response, which has not been sufficiently covered in the existing literature, despite its potential for the gas balancing market. Despite the existence of few research works and solutions regarding pure gas DR, the concept of an integrated demand response has been more widely researched, with the existing literature displaying promising results from the co-optimization of natural gas along with other energy sources, such as electricity and heat

Overview of Natural Gas Boiler Optimization Technologies and Potential Applications on Gas Load Balancing Services

Natural gas is a fossil fuel that has been widely used for various purposes, including residential and industrial applications. The combustion of natural gas, despite being more environmentally friendly than other fossil fuels such as petroleum, yields significant amounts of greenhouse gas emissions. Therefore, the optimization of natural gas consumption is a vital process in order to ensure that emission targets are met worldwide. Regarding residential consumption, advancements in terms of boiler technology, such as the usage of condensing boilers, have played a significant role in moving towards this direction. On top of that, the emergence of technologies such as smart homes, Internet of Things, and artificial intelligence provides opportunities for the development of automated optimization solutions, which can utilize data acquired from the boiler and various sensors in real-time, implement consumption forecasting methodologies, and accordingly provide control instructions in order to ensure optimal boiler functionality. Apart from energy consumption minimization, manual and automated optimization solutions can be utilized for balancing purposes, including natural gas demand response, which has not been sufficiently covered in the existing literature, despite its potential for the gas balancing market. Despite the existence of few research works and solutions regarding pure gas DR, the concept of an integrated demand response has been more widely researched, with the existing literature displaying promising results from the co-optimization of natural gas along with other energy sources, such as electricity and heat