Due to the uncertainty of renewable energy generation, frequency fluctuations and supply-demand imbalance issues of the power grid are becoming increasingly prominent. Since computing jobs can be allocated varying amounts of resources within service level agreement constraints, the data centers possess temporal flexibility in energy consumption. By leveraging this flexibility, data centers can support the grid by participating in ancillary service markets while earning additional revenue. However, the multi-time-scale requirements of grid regulation demand create challenges for joint market bidding and resource allocation strategies. In this paper, we propose a multi-time-scale decision-making approach for data center bidding and resource allocation to provide both frequency regulation and reserve services. First, we model the hour-ahead market bid, real-time power decision, and real-time resource allocation processes as three-layer Markov Decision Processes. Next, we develop a multi-time-scale optimization method based on hierarchical reinforcement learning to solve the proposed model. At the upper layer, the market bid decision is optimized using the Deep Deterministic Policy Gradient algorithm. Based on the bidding, the second layer determines the target operation power. Based on the target power, the third layer optimizes job resource allocation. Finally, we validate the effectiveness of the proposed method for data center market bidding and resource allocation through numerical experiments and analysis.

Accurate forecasting of Information Technology (IT) power demand is essential for the operation of data centers. However, the complex coupling relationships between various factors and IT power demand pose a challenge to its prediction. The widespread application of free cooling technology further highlights this problem, which makes it difficult for traditional models based only on CPU utilization and historical load to accurately forecast IT power demand at different time scales. To address these challenges, we propose DCITNet, an innovative end-to-end framework designed for IT power demand forecasting under the water-side free-cooling system. Specifically, we first extract the primary coupling mechanisms applicable for forecasting under water-side free-cooling conditions by analyzing the relationships between IT factors (e.g., CPU utilization), non-IT factors (e.g., environmental factors, auxiliary equipment), and IT power demand. Secondly, we extend these coupling mechanisms into three neural network modules. We establish two separate modules to capture the periodic coupling characteristics of IT factors and the time-lagged coupling characteristics of non-IT factors. Then we introduce a fusion module based on crossattention to integrate the coupling characteristics between IT factors and non-IT factors, which enables the precise forecasting of IT power demand. Finally, our proposed method shows significant improvements in accuracy through experiments on a realworld dataset and comparisons with state-of-the-art algorithms. Specifically, it improves the accuracy of short-term forecasting by more than 20% and improves the accuracy of mid- to long-term forecasting by around 50%.

With dual challenges from electricity market liberalization and the rapid integration of renewable energy sources (RES), power systems need enhance operational flexibility and control capabilities. Concurrently, electricity price volatility in market mechanisms increases consumer price sensitivity and the need for effective price-responsive demand-side management is emerging. In the paper, firstly, the operational principles of price-responsive load adjustment are analyzed and mathematical models for peak shaving, valley filling, and load shifting are established. Secondly, a time-series production cost simulation model based on Security-Constrained Unit Commitment (SCUC) and Security-Constrained Economic Dispatch (SCED) by integrating demand-side price-responsive control constraints is proposed. Finally, a regional power system case study is set up to verify and analyze the rationality and functionality of the model.

The participation of frequency regulation market for coal-fired generators not only ensures system security but also enhances their economic benefits through ancillary services. However, multiple economic factors including generation revenue, ancillary service revenue and generation cost must be considered to maximize profits. In the paper, a method for decision-making of frequency regulation market participation for coal-fired generators operating in power spot markets coupled with frequency regulation market is established. A market simulation model of co-optimizations of energy and frequency regulation market is used of the generator by comparing daily economic profitability difference for the scenarios whether or not participation of frequency regulation market. Case studies using an actual provincial spot market is performed to validate the method.

Under the background of the electricity market reform, generators are required to refine their bidding strategies to improve their own profitability. A bidding optimization method is proposed in this paper for thermal generators that simultaneously optimizes bid segment prices and capacities to maximize profit. The method employs a hybrid Particle Swarm Optimization–Gray Wolf Optimization algorithm within an iterative framework. In each iteration, the generator’s daily revenue is calculated from the forecasted price profile and piecewise bid segments, generation costs are estimated using a quadratic cost model, and operational constraints are applied via penalties. The hybrid algorithm efficiently solves optimization problems in high-dimensional search spaces. Case studies on different market operation days demonstrate that the proposed approach yields higher daily profits, thereby validating the effectiveness of the model presented in this paper. A sensitivity analysis further shows that coal price fluctuations markedly affect optimal bidding outcomes, underscoring the need to adapt strategies under fuel cost uncertainty.

In power spot market, the volatility of spot electricity prices and the mismatch between the output profiles of large-scale renewable energy bases and load characteristics of power-importing province have increased the complexity of economic evaluation for large-scale bases. This paper investigates these challenges by developing a set of evaluation indicators that consider both revenue mechanisms and cost structures. A spot market simulation model is proposed for large-scale renewable bases participating in provincial electricity markets through a "point-to-grid" mode, allowing for the quantitative assessment of economic performance. A case study involving a large-scale base connected to a provincial power system in China demonstrates that key economic indicators deteriorate as the long-term physical contract energy decreases. This indicates that, under current market transactions, the economic incentives for renewable bases to participate in electricity markets remain insufficient. The findings highlight the need for improved policy mechanisms to enhance the competitiveness and market participation of large-scale renewable energy bases, ensuring their sustainable integration into the power system.

With the rapid expansion of variable renewable energy, coal-fired units are increasingly operated at low load, where non-convex cost characteristics pose challenges for spot market clearing. This study reviews and improves existing low-load generation cost models, introducing three key enhancements: (1) integrating piecewise linearization with the marginal cost approach to reduce computational burden; (2) removing redundant binary variables and incorporating previously omitted cost components to improve clearing efficiency; and (3) developing a fuel cost model that combines quasi-fixed and marginal costs for low-load generation with firing and combustion support (FCS), enabling the joint optimization of low-load and normal operations. Applied to 6-bus and provincial systems, the proposed approach achieves speed-ups of 11.3× and 6.3× over the benchmark model (Model I) while maintaining accuracy, demonstrating both its efficiency and practical applicability.

Under the framework of power spot market construction, the bidding strategy of large-scale renewable energy bases has become a primary factor influencing their economic performance. This paper introduces a novel spot market bidding strategy that is developed through a bi-directional iterative process including a market clearing model of the power market and an optimization model of the renewable base. The method considers both the capability of renewable energy accommodation and the impact of large bases on nodal prices in receiving provinces. With the objective of revenue maximization, the model incorporates physical constraints related to energy storage, receiving-end renewable accommodation capacity. Iterative computation ensures the convergence of bidding schemes and market results. Case study reveal that in low-load provinces, large renewable base exert a significant influence on market prices, which requiring multiple iterations for optimal bidding profile. Whereas in highload provinces, a single iteration suffices. The proposed approach offers a feasible and effective trading method for renewable energy base to participate in spot markets.

As global climate changing, the likelihood of extreme weather occurrences is increasing. Traditional reliability assessment methods often overlook the impact of extreme weather events on power systems. A method for calculating the reliability of power systems considering extreme weather conditions is proposed in the paper. By constructing extreme weather years and considering the effects of extreme heat with few wind and extreme cold with no sunlight, the generation characteristics of renewable resources and the load profile are adjusted during periods of extreme weather. A monte Carlo simulation is modeled for simulating the random events of power system operation to evaluate reliability indices. In the case study, reliability indices of a power system are computed through Monte Carlo simulation to assess whether the installed capacity meets the reliability criteria of the power system during extreme weather conditions. Consequently, generation expansion is performed to ensure the reliability of the power system.

To achieve carbon peaking and carbon neutrality goals, power system is facing the stability challenges posed by the large-scale integration of renewable energy. It is necessary to build a certain scale of flexibility resources by means of co-planning in order to adapt to the enormous growth of renewable energy installations. Firstly, the principles of three types of flexibility resources in new power system is described in the paper, and a mathematical model is proposed to describe the regulation process of the flexibility resources. Secondly, a new power system generation-load-storage co-planning model considering multiple types of flexibility resources is constructed. Finally, this model is combined with a long-term time-series production simulation model to verify the feasibility of the planning results.

由于可再生能源具有高度的随机性和波动性,传统的基于确定性场景的电力系统生产模拟技术,已难以适用于电力系统复杂、多维度、不确定性的运行形态.首先,从风电、光伏等可再生能源出力随机性描述入手,通过随机微分方程模型对其随机出力现象进行数学分析,并在此基础上对其时序出力模型进行重构.然后,针对传统时序模拟中难以处理的跨周、跨月问题,提出了含水、火、风、光多能互补的协调优化策略;并以该策略为基础,考虑各种发电机组的发电成本、机组启停成本以及弃风、弃光、弃水、切负荷的损失成本,构建出考虑可再生能源大规模接入的电力系统时序生产仿真模型.最后,以Q省2023年规划方案对该仿真模型进行验证,结果证实了该模型的可行性与有效性.

This study designs and proposes a method for evaluating the configuration of energy storage for integrated renewable generation plants in the power spot market, which adopts a two-level optimization model of “system simulation + plant optimization”. The first step is “system simulation” which is using the power market simulation model to obtain the initial nodal marginal price and curtailment of the integrated renewable generation plant. The second step is “plant optimization” which is using the operation optimization model of the integrated renewable generation plant to optimize the charge-discharge operation of energy storage. In the third step, “system simulation” is conducted again, and the combined power of renewable and energy storage inside the plant is brought into the system model and simulated again for 8,760 h of power market year-round to quantify and compare the power generation and revenue of the integrated renewable generation plant after applying energy storage. In the case analysis of the provincial power spot market, an empirical analysis of a 1 GW wind-solar-storage integrated generation plant was conducted. The results show that the economic benefit of energy storage is approximately proportional to its capacity and that there is a slowdown in the growth of economic benefits when the capacity is too large. In the case that the investment benefit of energy storage only considers the income of electric energy-related incomes and does not consider the income of capacity mechanism and auxiliary services, the income of energy storage cannot fulfill the economic requirements of energy storage investment.

With the increase of the scale of highly uncertain power supply in the system, the system needs more and more energy flexibility. Existing studies focus more on short-term energy demand volatility changes and lack quantitative means to analyze the system’s demand for energy flexibility at different time scales. Aiming at the above problems, this paper proposes an energy flexibility demand analysis and evaluation method for power systems with a high proportion of new energy. It constructs an energy flexibility demand evaluation index system at different time scales. Combined with the analysis of domestic provincial cases, the results verify the effectiveness of the proposed method.

Concentrating solar power has drawn continuous attention from generation companies as a renewable generation technology. In this paper, an economic evaluation method for PV-CSP plants is constructed employing LCOE as the primary economic index. A chronological market simulation model of the power spot market based on SCUC and SCED with the objective of minimizing system production cost is used for the method. An energy flow-based model of PV-CSP plant is constructed for simulating the optimal operation of PV-CSP plants in the power spot market. The output of market simulation is applied for the PV-CSP plant model to provide price signal and curtailment information that is a basis of optimal operation of thermal energy storage. The economic viability of PV-CSP plants is assessed using LCOE. A comparative analysis for 4 sets of PV and CSP plants demonstrates that PV-CSP plants exhibit lower LCOE than CSP plants. The proposed economic evaluation method provides a comprehensive method for economic evaluations of the PV-CSP plant for generation companies.

Renewable generation is rapidly developing along with dual-carbon strategy. Power spot markets are established in 8 provinces in terms of national market reform of power industry. Accordingly, conventional power system planning should be adjusted to accommodate to the new era power system. In the paper, an optimal integrated planning method in the purpose of meeting renewable accommodation target with consideration of economic impact to the spot market is proposed. A chronological production cost simulation model with security constraint unit commitment and locational marginal price calculation is used to simulate the power spot market. A root-cause analysis of renewable curtailment based on simulation results is designed to initially determine planning alternatives including transmission upgrades and energy storage systems. Optimal integrated planning is achieved based on the quantitative analysis of economic impact of planning alternatives using the simulation model. A case study using an actual provincial power system with spot market environment shows that the proposed planning method is able to provide the economically reasonable planning alternative with meeting renewable accommodation target.

工业大用户是一个多能源综合供给系统,传统的工业大用户综合能源系统(integrated energy system,IES)规划忽略了多种能源的参与.计及冷热电联供系统(combined cooling,heating and power,CCHP),考虑多能梯级利用及其与可转移生产任务的耦合关系,文章建立了工业大用户综合能源系统扩展规划模型.目标函数为系统综合成本最低,其中包括与售电公司交互成本、大用户自发电成本及生产任务转移成本,模型中考虑了多能耦合平衡约束、发电设备技术约束、可转移生产任务负荷调度约束等约束条件,优化CCHP的容量配置.该模型是一个混合整数线性规划(mixed integer linear programming,MILP)模型.最后通过算例对所提模型进行验证,结果表明在工业大用户综合能源系统规划中考虑CCHP能够提高用户用能的灵活性,有效降低综合成本.

This paper studies the route assignments and charging scheduling of electric bus fleet under centralized control. Specifically, we optimize the charging scheduling decisions for the electric bus fleet to minimize the daily scheduling cost which includes the deadheading travel cost and charging cost. The charging cost is characterized by a time-varying locational pricing scheme. A mixed integer linear programing (MILP) model is formulated to efficiently solve the proposed fleet management and charging scheduling problem. Also, we provide a numerical case study based a real-world dataset to illustrate the effectiveness of the proposed model.

Power market is booming in China right after the 9th order. Conventionally, transmission planning is performed mainly based on purpose of transmission security in a regulated environment. However, when power market takes place, economic planning of transmission system should be carefully designed and considered, since transmission infrastructure could heavily impact the efficiency of the power market, as well as the benefit of all kinds of market participants. Transmission economic planning had been developed in almost all mature power markets overseas. In the paper, such practices in several RTOs/ISOs in United States and TSOs in Europe are investigated. The implementation process, evaluate method and key economic benefit indices of transmission economic planning are summarized in the paper. In the end, based on the current transmission planning and the development of power market in China, the suggestions how transmission economic planning should be performed is given.

Transmission planning is a traditional and important activity in Grid Company in China and its security related technologies had been developed over the years. In resent 5 years, there are unprecedented change in power industry in China, including market reform, 3060 dual carbon strategy, national carbon market, and so on. Therefore, transmission planning should be promoted to accommodate the needs of the new era. Especially, economic benefit should be carefully considered in the transmission planning process. In the paper, a new method of economic benefit evaluation of transmission planning is proposed. A production cost simulation is used to quantify the economic benefit. And impacts of spot market and carbon market are considered in the simulation model. In the end, a case study using actual power system is performed to quantity market-wise economic benefit of transmission upgrade. The study shows that transmission upgrade can reduce load payment, meanwhile, carbon market significantly affects economic benefit of transmission upgrade.

With the rapid growth of energy storage systems, the various advantages such as high response rate, switchable charging and discharging mode are significant for power grid control and renewable energy accommodation. This paper designs a technical framework and market model of energy storage systems incorporated into the optimal control of power grid. A practical optimal control platform of dispatch center of North China Grid is built to realize the interaction and closed-loop control of energy storage aggregators. The mode design of energy storage aggregators participating in the valley-filling market is also descripted in this paper. Based on practice by the North China Grid, the proposed strategy is verified that it can benefit both traditional generator and energy storage aggregators. With the commercial operation of energy storage aggregators, the valley-filling capability of power grid is improved, and further development of technical and commercial value of energy storage systems can be realized.