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Attaining highly secure and safe operation of the grid with acceptable voltage levels has become a difficult issue for electricity companies that must adopt remedial actions. The usage of a PV solar farm inverter as a static synchronous compensator (or PVSTATCOM device) throughout the night has recently been proposed as a way to enhance the system performance. In this article, the novel artificial rabbits’ optimization algorithm (AROA) is developed for minimizing both the daily energy losses and the daily voltage profile considering different 24 h loadings. The novel AROA is inspired from the natural surviving strategies of rabbits. The novel AROA is tested on a typical IEEE 33-node distribution network including three scenarios. Different scenarios are implemented considering PV/STATCOM allocations throughout the day. The effectiveness of the proposed AROA is demonstrated in comparison to differential evolution (DE) algorithm and golden search optimization (GSO). The PVSTATCOM is adequately allocated based on the proposed AROA, where the energy losses are greatly reduced with 54.36% and the voltage deviations are greatly improved with 43.29%. Moreover, the proposed AROA provides no violations in all constraints while DE fails to achieve these limits. Therefore, the proposed AROA shows greater dependability than DE and GSO. Moreover, the voltage profiles at all distribution nodes all over the daytime hours are more than the minimum limit of 95%.

Virtual power plants (VPPs) are an effective platform for attracting private investment and customer engagement to speed up the integration of green renewable resources. In this paper, a robust bidding strategy to participate in both energy and ancillary service markets in the wholesale electricity market is proposed for a realistic VPP in Western Australia. The strategy is accurate and fast, so the VPP can bid in a very short time period. To engage customers in the demand management schemes of the VPP, the gamified approach is utilized to make the exercise enjoyable while not compromising their comfort levels. The modelling of revenue, expenses, and profit for the load-following ancillary service (LFAS) is provided, and the effective bidding strategy is developed. The simulation results show a significant improvement in the financial indicators of the VPP when participating in both the LFAS and energy markets. The payback period can be improved by 3 years to the payback period of 6 years and the internal rate of return (IRR) by 7.5% to the IRR of 18% by participating in both markets. The accuracy and speed of the proposed bidding strategy method is evident when compared with a mathematical method.

Sustaining a high-renewable electricity generation portfolio has proved to be difficult due to grid flexibility constraints. We utilize the abrupt decline in electricity demand due to the COVID-19 lockdown in China as a policy experiment to study the role of ancillary service markets (ASMs) in promoting intermittent renewable energy integration. Our results reveal that provinces with ASMs sustained a significantly higher generation of intermittent renewable energy during the COVID-19 demand shock, which amounts to 9.7 percent of average monthly intermittent renewable energy generation during the shock. A back-of-envelope analysis further shows that annual carbon emission offset from establishing ASMs in all provinces of China could amount to 13 to 17 percent of annual carbon emissions from Britain in the near future when renewable energy penetration continues to go up. JEL Classification: Q20, Q42, Q53, O13

The high penetration of distributed renewable energy raises a higher concern for the safe and economic operation of the smart grid. Distributed batteries equipped in demand-side can not only contribute to the reliability and security of the grid, but also make profits by participating in the electricity market, especially when distributed batteries are combined and operated by an aggregator. Considering the well-operated mechanism of performance based regulation (PBR) in the U.S. electricity market, it becomes increasingly lucrative for batteries to participate not only in energy markets for energy arbitrage, but also in ancillary service markets to provide regulation and peak-load shaving services. In this study, distributed batteries are operated and coordinated by the aggregator, which simultaneously submits offers to the energy and the ancillary service markets as an individual entity. An optimal decision model is formulated for the aggregator to determine the operation and bidding strategy for the distributed batteries by considering the characteristics of batteries, including the terms of capacity, efficiency and degradation cost. Finally, a numerical case is conducted to evaluate the benefits of the decision model.

China states to build new power system dominated by new energy power to promote the targets for peaking carbon emissions by 2030 and achieve carbon neutrality by 2060. Peaking regulation ancillary services provided by coal-fired power units is an essential solution to mitigate the volatility and instability of large-scale renewable energy for China’s specific power mix. However, when the coal-fired power units operate at a low power output, the intensity of both coal consumption and carbon emissions gradually rises with the falling output rate. Moreover, cutting down the power output of coal-fired units frequently will damage the technical life. Given the impacts of power market reform and carbon mitigation targets, whether to participate in the energy market or the peaking regulation ancillary service market is an urgent issue for coal-fired power units. Considering the discrepancy in costs and benefits of various units at different output rate, this paper proposes a multi-objective optimization model to solve the issue from the perspective of the coal-fired power generators, in which both economic profit and carbon reduction goals are coordinated. Sequential quadratic programming is adopted to solve the nonlinear optimization problem. In order to study the difference in the decisions made by varied technical units, 7 different types of units are analyzed in the case study. The scenarios analysis indicates that large-capacity and new coal-fired power units are better to participate in energy market since it can give full play to the advantage of higher generation efficiency, while the small-capacity ones are suitable to provide flexible service in the peaking regulation ancillary service market. Besides, simple low-carbon objective will burden the cost of coal-fired power units and challenge the sustainable transition of power system. Hence, the power system should balance both economic profit of generators and national carbon mitigation targets during the low-carbon transition.

Flexibility is crucial to enable the penetration of high shares of renewables in the power system while ensuring the security and affordability of the electricity dispatch. In this regard, heat–electricity sector coupling technologies are considered a promising solution for the integration of flexible devices such as thermal storage units and heat pumps. The deployment of these devices would also enable the decarbonization of the heating sector, responsible for around half of the energy consumption in the EU, of which 75% is currently supplied by fossil fuels. This paper investigates in which measure the diffusion of district heating (DH) coupled with thermal energy storage (TES) units can contribute to the overall system flexibility and to the provision of operating reserves for energy systems with high renewable penetration. The deployment of two different DH supply technologies, namely combined heat and power units (CHP) and large-scale heat pumps (P2HT), is modeled and compared in terms of performance. The case study analyzed is the future Italian energy system, which is simulated through the unit commitment and optimal dispatch model Dispa-SET. Results show that DH coupled with heat pumps and CHP units could enable both costs and emissions related to the heat–electricity sector to be reduced by up to 50%. DH systems also proved to be a promising solution to grant the flexibility and resilience of power systems with high shares of renewables by significantly reducing the curtailment of renewables and cost-optimally providing up to 15% of the total upward reserve requirements.

Over the period 2016–2021 Australia’s National Electricity Market (NEM) experienced an investment supercycle with 16,000MW of new utility-scale renewable plant commitments in a power system with a peak demand of 35,000MW, and the disorderly loss of 5,000MW of synchronous coal-fired plant. This placed strains on system security, most visibly in the distribution of the power systems’ frequency, requiring material changes to the NEM’s suite of Frequency Control Ancillary Service (FCAS) markets. Utility-scale batteries are ideally suited for FCAS duties, but there is no forward price curve for FCAS markets, nor is there any systematic framework for determining equilibrium prices that might otherwise be used for investment decision-making. In this article, we develop an approach for quantifying long run equilibrium costs and stochastic spot prices in the markets for Frequency Control Ancillary Services, with the intended application being to guide the suitability of utility-scale battery investments under conditions of uncertainty and missing forward FCAS markets.

This paper proposed a novel integrated system with solar energy, thermal energy storage (TES), coal-fired power plant (CFPP), and compressed air energy storage (CAES) system to improve the operational flexibility of the CFPP. A portion of the solar energy is adopted for preheating the boiler’s feedwater, and another portion is stored in the TES for the CAES discharging process. Condensate water from the CFPP condenser is used for cooling compressed air during the CAES charging process. The thermodynamic performance of the integrated system under different load conditions is studied. The system operations in a typical day are simulated with EBSILON software. The system enables daily coal saving of 9.88 t and reduces CO 2 emission by 27.95 t compared with the original CFPP at 100% load. Under partial load conditions, the system enables maximum coal saving of 10.29 t and maximum CO 2 emission reduction of 29.11 t at 75% load. The system has maximum peak shaving depth of 9.42% under 40% load condition. The potential of the system participating ancillary service is also discussed. It is found that the integration of solar thermal system and CAES system can bring significant ancillary service revenue to a conventional CFPP.

The active distribution network (ADN) can provide the reactive power ancillary service (RPAS) to improve the operations of the transmission network operations (such as voltage control and network loss reduction) as distribution generation grows. In this context, an RPAS market is required to motivate the ADN to provide the RPAS to the transmission network since the transmission system operator (TSO) and the distribution system operator (DSO) are different entities. Hence, to obtain the TSO–DSO coordination in the RPAS market, this study proposes a two‐stage market framework on the basis of the successive clearing of the energy and RPAS markets. Additionally, a distributed market‐clearing mechanism based on an alternating direction method of multipliers (ADMM) is adopted to guarantee TSO's and DSO's information privacy. Furthermore, a binary expansion (BE) method is used to linearise the non‐convex bilinear terms in the market‐clearing model. The effectiveness of the proposed RPAS market framework and distributed market‐clearing mechanism is validated using two different test systems with different system scales.

Reactive power has gained acknowledgment as an ancillary service, indispensable for generators to ensure the dependable process of power systems. Moreover, it carries the potential to significantly enhance the effectiveness of delivering active power to consumers. Additionally, a strategic injection of reactive power at specific points holds the capability to alleviate transmission constraints, enabling cost-effective power distribution to heavily loaded regions. Given the inherent characteristics of reactive power provision, it necessitates efficient and dependable local procurement and management strategies. However, prevailing models fail to encompass the diverse dimensions of reactive power costs, leading to inefficiencies. Thus, an optimal resolution is imperative to rectify these complexities. This study introduces an innovative market mechanism for delivering reactive power ancillary services (RPAS), with a specific emphasis on its localized implementation. The proposed approach leverages the Sailfish Optimizer and is implemented on the General Algebraic Modeling System platform. The effectiveness of this novel mechanism is demonstrated through application to the IEEE 30-bus and PJM 5-bus systems, incorporating wind energy sources. The objective function takes into account the critical role of reactive power in maintaining bus voltage within acceptable limits and evaluates the availability of reactive power reserves within the system. The adoption of this utility function results in a noteworthy reduction in the total payment associated with RPAS, concurrently leading to an improvement in system-wide bus voltage and the preservation of necessary reactive power reserves across the network.