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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.

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.

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%.

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.

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.

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.

Energy storage can further reduce carbon emission when integrated into the renewable generation. The integrated system can produce additional revenue compared with wind-only generation. The challenge is how much the optimal capacity of energy storage system should be installed for a renewable generation. Electricity price arbitrage was considered as an effective way to generate benefits when connecting to wind generation and grid. This wind-storage coupled system can make benefits through a time-of-use (TOU) tariff. A proportion of electricity is stored from the wind power system at off-peak time (low price), and released to the customer at peak time (high price). Thus, extra benefits are added to the wind-storage system compared with wind-only system. A Particle Swarm Optimization (PSO) algorithm based optimization model was constructed for this integrated system including constraints of state-of-charge (SOC), maximum storage and release powers etc. The proposed optimization model was to obtain the optimal capacity of energy storage system and its operation control strategy of the storage-release processes, to maximize the revenue of the coupled system considering the arbitrage. Furthermore, the energy storage can provide reserve ancillary services for the grid, which generates benefits. The benefits of energy storage system through reserve ancillary services were also calculated. A case study was analyzed with respect to yearly wind generation and electricity price profiles. The benefit compared with no energy storage scenario was calculated. The impact of the energy storage efficiency, cost and lifetime was considered. The sensitivity and optimization capacity under various conditions were calculated. An optimization capacity of energy storage system to a certain wind farm was presented, which was a significant value for the development of energy storage system to integrate into a wind farm.

A novel non-linear stochastic method based on a Mixed-Integer Linear Programming (MILP) optimization model is proposed to optimally manage a high number of photovoltaic (PV)-battery systems for the provision of up and down regulation in the ancillary services market. This method, considers both the technical constraints of the power system, and those of the equipment used by all the prosumers. This allows an aggregator of many residential prosumers endowed with photovoltaic (PV)- battery systems to evaluate the baseline of the aggregate by minimizing the costs related to the electrical energy absorbed from the grid and then to assess the up and down flexibility curves with relative offer prices. As confirmed by simulation results carried out considering different realistic case studies, the method can effectively be used by an aggregator to evaluate the economic impact of its participation in the ancillary services market, both for the aggregator and for its prosumers.

Airlines have launched various ancillary services to meet their passengers’ requirements and to increase their revenue. Ancillary revenue from seat selection is an important source of revenue for airlines and is a common type of advertisement. However, advertisements are generally delivered to all customers, including a significant proportion of people who do not wish to pay for seat selection. Random advertisements may thus decrease the amount of profit generated since users will tire of useless advertising, leading to a decrease in user stickiness. To solve this problem, we propose a Bagging in Certain Ratio Light Gradient Boosting Machine (BCR-LightGBM) to predict the willingness of passengers to pay to choose their seats. The experimental results show that the proposed model outperforms all 12 comparison models in terms of the area under the receiver operating characteristic curve (ROC-AUC) and F1-score. Furthermore, we studied two typical samples to demonstrate the decision-making phase of a decision tree in BCR-LightGBM and applied the Shapley additive explanation (SHAP) model to analyse the important influencing factors to further enhance the interpretability. We conclude that the customer’s values, the ticket fare, and the length of the trip are three factors that airlines should consider in their seat selection service.

Energy storage is currently a key focus of the energy debate. In Germany, in particular, the increasing share of power generation from intermittent renewables within the grid requires solutions for dealing with surpluses and shortfalls at various temporal scales. Covering these requirements with the traditional centralised power plants and imports and exports will become increasingly difficult as the share of intermittent generators rises across Europe. Pumped hydropower storage plants have traditionally played a role in providing balancing and ancillary services, and continue to do so. However, the construction of new plants often requires substantial interventions into virgin landscape and bio-habitats; this is often fiercely opposed by local citizens. Utility-scale lithium ion batteries have recently entered the energy scene. Albeit much smaller than most pumped hydropower plants, they can also provide the required balancing and ancillary services. They can be constructed on brownfield sites as and where needed, to support the move towards increasingly decentralised energy systems. Although they are seen by some as a more environmentally friendly option, they do cause impacts relating to the consumption of limited natural resources during the production stage. Addressing initially technological capacity of pumped hydropower storage and utility-scale battery to meet the required services, a simplified LCA will be performed to examine the environmental impacts throughout their life cycles. This includes two sensitivity analyses. Issues addressed in this paper include also methodological issues relating to comparability and those parameters that are pivotal to the LCA result.