Peak Regulation Research Articles

Optimal bidding strategy for virtual power plant participating in combined electricity and ancillary services market considering dynamic demand response price and integrated consumption satisfaction

The virtual power plant (VPP) plays an important role in managing distributed energy by integrating renewable energy sources, energy storage systems and dispatchable loads. It can not only provide peak regulation services as good flexible resources, but also participate in the electricity market for additional profit. This paper presents a multimarket model to develop an optimal bidding strategy for VPP. To enhance the effectiveness of demand response, a fixed time of use price is converted into a dynamic response price. The integrated consumption satisfaction is quantified from both comfort and economy perspectives. Multi-objective optimization is carried out to maximize market profit of VPP as well as consumer satisfaction. The results show that: (1) Compared to time of use prices, VPP's profit and consumers' satisfaction level increased by 12.46% and 3.26% respectively under dynamic response prices. (2) The increase in the market profit of VPP is accompanied by a gradual decline in integrated consumption satisfaction. There are two inflection points in the process of decline, occurring at satisfaction levels of 1.01 and 0.98 respectively. (3) The multi-objective optimization strategy can achieve a win-win situation for both its operator and the internal consumers.

Optimization of Circulating Fluidized Bed Boiler Combustion Key Control Parameters Based on Machine Learning

During the coal-fired circulating fluidized bed unit participation in the peak regulation process of the power grid, the thermal automatic control system assists the operator to adjust the mode focusing on pollutant control and ignoring the economy so that the unit’s operating performance maintains a huge potential for deep mining. The high-dimensional and coupling-related data characteristics of circulating fluidized bed boilers put forward more refined and demanding requirements for combustion optimization analysis and open-loop guidance operation. Therefore, this paper proposes a combustion optimization method that incorporates neighborhood rough set machine learning. This method first reduces the control parameters affecting multi-objective combustion optimization with the neighborhood rough set algorithm that fully considers the correlation of each variable combination and then establishes a multi-objective combustion optimization prediction model by combining the online calculation of boiler thermal efficiency. Finally, the NSGAII algorithm realizes the optimization of the control parameter setting value of the boiler combustion system. The results show that this method reduces the number of control commands involved in combustion optimization adjustment from 26 to 11. At the same time, based on the optimization results obtained by using traditional combustion optimization methods under high, medium, and medium-low load conditions, the boiler thermal efficiency increased by 0.07%, decreased by 0.02%, and increased by 0.55%, respectively, and the nitrogen oxide emission concentration decreased by 5.02 mg/Nm3, 7.77 mg/Nm3, and 7.03 mg/Nm3, respectively. The implementation of this method can help better account for the economy and pollutant discharge of the boiler combustion system during the variable working conditions, guide the operators to adjust the combustion more accurately, and effectively reduce the ineffective energy consumption in the adjustment process. The proposal and application of this method laid the foundation for the construction of smart power plants.

Combining integrated solar combined cycle with wind-PV plants to provide stable power: Operation strategy and dynamic performance study

Building a multi-energy complementary power generation system is a viable way to encourage the use of renewable energy and decarbonize power generation. However, the intermittent nature of renewable power generation, such as photovoltaic and wind power, has prompted concerns regarding power grid stability. To balance such fluctuations, energy storage systems or other flexible power generation technologies should be integrated. In this paper, the peak regulation ability of integrated solar combined-cycle has been enhanced via employing a gas/oil exchanger between the top and bottom cycle. When integrating high penetration intermittent renewable energy, an appropriate operational strategy towards high-quality steady power output regulation is proposed. Dynamic performance analysis of the system, coupled characteristic of heat and mass transfer between subsystems have been highlighted. The case study demonstrates that fluctuations of the multi-energy complementary system power output can be controlled below 0.3 MW without renewable energy curtailment, even though wind-PV power generation fluctuates from 0.3 MW to 26.1 MW with variations per second reaching −2.8/3.7 MW. Furthermore, the system's levelized cost of electricity is down to 0.0512 $/kWh, which is cost-competitive with conventional power generation technologies.

Optimized Power and Capacity Configuration Strategy of a Grid-Side Energy Storage System for Peak Regulation

The optimal configuration of the rated capacity, rated power and daily output power is an important prerequisite for energy storage systems to participate in peak regulation on the grid side. Economic benefits are the main reason driving investment in energy storage systems. In this paper, the relationship between the economic indicators of an energy storage system and its configuration is first analyzed, and the optimization objective function is formulated. Then, according to the objective limitations of the energy storage system configuration and operation, the constraints are formulated. A set of typical parameters is selected, and the CPLEX (IBM ILOG CPLEX Optimization Studio) solver is used in MATLAB to solve the optimal configuration results. Several sets of optimization results are obtained by taking different subjective coefficient β values, and the economic and social benefits of the optimization results are analyzed. When the economic benefits of the energy storage system are more important, the value of β needs to be smaller, such as a value of 1000. Conversely, when the peak-regulation effect is more important, the value of β should be larger. Finally, configuration results of the control groups are given, and the effect of optimizing the calculation for improving economic benefits is verified.

Experimental and Numerical Study of the Ice Storage Process and Material Properties of Ice Storage Coils

The coiled ice-storage-based air conditioning system plays a significant role in enhancing grid peak regulation and improving cooling economy. This paper presents theoretical and experimental studies conducted on the ice storage process of coiled ice storage air conditioning technology. The cooling of water is divided into two stages:10.0 °C to 4.0 °C and 4.0 °C to below 0.0 °C. Initially, the ice storage process forms an ice layer with a thickness of 2.50 mm on the lower surface of the coil, but eventually, the ice layer on the upper surface becomes 3.85 mm thicker than the lower surface as a result of the natural convection of water and density reversal at 4.0 °C. Furthermore, the impact of three coils with different thermal conductivity on the ice storage process was evaluated. It was observed that the thermal conductivity of R-HDPE (reinforced high-density polyethylene) was only 2.6 W/(m·K) higher than HDPE (high-density polyethylene), yet it reduced the freezing time by 34.85%, while the thermal conductivity of steel was 37.4 W/(m·K) higher than R-HDPE, but only decreased the freezing time by 9.40%. The results demonstrated that the rate of ice accumulation increased with thermal conductivity. However, when the coil material’s thermal conductivity surpassed that of ice, the further increase of thermal conductivity gradually weakened its impact on the ice storage rate.

Energy consumption optimization method of wireless sensor information collection network for new energy scheduling

New energy integration is thought to be one of the most potential solutions to support the power system with a sustainable energy infrastructure. However, new energy is an uncertain power generation resource, and the electricity generated by it has the characteristics of randomness, intermittency and reverse peak regulation. Its large-scale integration into the power grid makes the operation and reliability scheduling of the power system more challenging. It was important to build a wireless sensing and monitoring network to monitor the power and change trend of the new energy field (station) in real time. The energy consumption of wireless sensing monitoring network is an important factor to improve the reliability of new energy scheduling. Based on the energy consumption of the wireless sensing monitoring network built by the new energy scheduling, the compression sensing technology was integrated and the network routing protocol (I-LEACH protocol) was optimized. The sampling data was transmitted by the cluster head node at the compression rate of 0.6, the improved OMP (Orthogonal Matching Pursuit) algorithm was reconstructed to achieve reliable data transmission, and the network energy consumption was further reduced. Compared with the I-LEACH routing protocol network, the experiments show that the network residual energy of the proposed method increased by 22% and the life cycle increased by about 30%. This method is helpful to improve the reliability of new energy power dispatching system and it can provide reference for realizing the reliability scheduling of new energy power system.

Carbon reduction analysis of electric heat pumps in carbon neutrality in China

Electric heat pumps align with the trend towards electrification of end-use energy in achieving carbon neutrality. To exhibit the carbon reduction potential of applying electric heat pumps in medium-low temperature heating in China, a carbon emission model considering variations in demand, green electricity, and heat pump scale was established. Results show that under the high electric heat pump growth scenario, the total carbon reductions from buildings, industries, and agriculture sectors in 2060 are 2.506 billion tons, with electric heat pumps contributing 1.453 billion tons which is equivalent to 14.7% of China's total current carbon emissions. Compared to direct electric heating, heating by electric heat pumps can reduce national electricity loads by 67.6% and annual electricity consumption by 64.9% in 2060. Furthermore, electric heat pumps can be actively applied to realize electricity-grid peak regulation, and the leakage of refrigerants should be restricted. Electric heat pumps are, therefore, an essential option for a carbon neutrality roadmap in medium-low temperature heating. Policies should be recommended to promote heat pump application in various sectors and reduce refrigerant greenhouse gas emissions.

Evaluation index system and evaluation method of energy storage and regional power grid coordinated peak regulation ability

With the development of energy storage technology, energy storage technology began to be put into the peak regulation of power grid. But at present, the lack of scientific evaluation means for coordinated peak regulation ability of energy storage and regional power grid (ESRPG) hinders the large-scale participation of energy storage devices in peak regulation. To solve this problem, this paper proposes an evaluation system and evaluation method to comprehensively and accurately evaluate the coordinated peak regulation ability of ESRPG. Firstly, an evaluation system for the coordinated peak regulation ability of ESRPG is established from the perspective of energy storage and generator units. The indicator system comprehensively considers the impact of technology, economy, reliability, and other factors on the peak regulation ability of the ESRPG. At the same time, a multi-level evaluation method composed of an analytic hierarchy process and weighted rank sum ration is proposed to comprehensively evaluate the proposed indicator system. This method not only takes subjective and objective factors into account, but also eliminates the interference of dimensions, orders of magnitude and other factors on the evaluation results. Finally, the evaluation indicator system and evaluation method are applied to the simulation scene for evaluation, and the results show that the evaluation system and method make up for the shortcomings of the current mainstream methods and effectively evaluate the peak shaving capability.

Control Technology of Smart Grid Considering Intermittent Power Uncertain Dispatch

The phenomenon of “anti-peak regulation” occurs when large-scale new energy is connected to the grid. The strange phenomenon of wind, light, and water curtailment at low loads occurs in areas with weak power regulation capacity, and the problem of peak regulation of the power system has become increasingly prominent. The typical scenario method is adopted to simplify the description of photovoltaic power generation output and hydropower station runoff. Then, considering the fluctuation of power production and output of the water and photovoltaic complementary system, the short-term multi-objective optimal dispatching model of the water and the photovoltaic complementary system is constructed with the objective of maximizing the average capacity coefficient and power generation load rate of the system on the premise of meeting the corresponding hydropower constraints. Finally, the simulation experimental platform is built. The multi-objective algorithm is used to solve the model. Through the case study, the complementary effect of intermittent power uncertain dispatching is analyzed, and the effectiveness of the model is verified. The dispatching mode can fully guarantee the collaborative optimization decision-making problem of a multi-microgrid with clean power. It can also effectively promote the acceptance level of clean power in the distribution network, and reduce the carbon emission of the distribution network.

Day-ahead multi-objective optimization of CHP unit with heat storage considering flexibility

Nowadays, with the growing number of renewable energy capacities, the flexibility of the energy system has become an indicator that must be considered under the impact of frequent peak regulation. However, more flexibility always means less economy. The purpose of this paper is to tackle the contradiction between flexibility and the economy in the energy system. Based on day-ahead load prediction, a multi-objective optimization method is proposed here considering the objectives of excess of power variation and operation cost. NSGA-II is used to deal with the optimization problem by forming a Pareto frontier containing all the optimized results. We take the microgrid system composed of two combined heat and power units and a molten salt heat storage tank as a case study. Two typical results that emphasize flexibility and economic indicator are analyzed respectively and compared with each other, which reduce general guidance of the operation mode’s selection.

Simulation and analysis of a peak regulation gas power plant with advanced energy storage and cryogenic CO2 capture

Flexible gas power plants are subject to energy storage, peak regulations, and greenhouse gas emissions. This study proposes an integrated power generation system that combines liquid air energy storage (LAES), liquefied natural gas (LNG) cold energy utilization, gas power systems, and CO2 capture and storage (CCS) technologies, named the LAES-LNG-CCS system. The off-peak electricity can be stored in liquid air. During the peak period, air and gas turbines generate supplementary electricity. Both LNG chemical energy and cold energy were considered: the former was used for gas power plants, and the latter was used for LAES regasification and CCS processes. Based on the thermodynamic analysis, we evaluated the effects of the recovery pressure, CCS pressure, and combustion temperature on the system power consumption and efficiency. The results demonstrated that the system recovery pressure, CCS pressure, and combustion temperature had the greatest effects on system power generation. Round-trip efficiency (RTE) was significantly affected by combustion temperature. The largest exergy loss occurred in the gas power plant. The optimal system operating ranges of the system recovery pressure, CCS pressure, and combustion temperature were 6−10 MPa, 0.53−0.8 MPa, and 1,503−1,773 K, where the RTEs and ηEx,RS reached 55%−58.98% and 74.6%−76%, respectively. The proposed system can simultaneously achieve the synergistic functions of large-scale energy storage, multilevel energy utilization, peak regulation, and carbon emission reduction. It can also be widely used in advanced distributed energy storage applications in the future.

Collaborative optimization strategy of source‐grid‐load‐storage considering dynamic time series complementarity of multiple storages

AbstractThe multi‐scale flexibility coordination of multiple storages is a key technology to enhance the diversified regulation ability of the power system. This paper first considers the interaction mechanism of multi‐type storage peak regulation time sequences based on the Euclidian distance, dynamic time warping distance, and storage correlation distance. A matching index was proposed to consider the temporal correlation, overall distribution characteristics, and dynamic characteristics of the net load and energy storage. The multitype storage coordination mode, including battery storage, pumped storage, and electric vehicles, was formulated, and a collaborative optimal scheduling system architecture of source‐grid‐load‐storage (SGLS) was constructed. To attain a low‐carbon economy, a collaborative optimal scheduling model of SGLS considering the dynamic time‐series complementarity of multiple energy storage systems was constructed. The Nash equilibrium theory was used to achieve friendly interaction among the source, grid, load, and storage. Then, an improved transfer reinforcement learning algorithm for SGLS was proposed, which used reinforcement learning and transfer learning algorithms combined with K‐means clustering and dual‐structure experience pool technology. The test results of actual regional power grid data indicated that the proposed strategy can effectively reduce the economic and carbon treatment costs of the system and improve the absorption capacity of renewable energy.

Flexible operation of the pressurized water reactor nuclear power system using multi-model predictive control over a wide nonlinear operating range

The nonlinearity problem caused by the large load variation is a significant challenge for the pressurized water reactor nuclear power system (PWRNPS) to realize its flexible operation over a wide operating range when it participates in peak regulation in the power grid. Thus, this work proposes a novel multi-model predictive control (MMPC) strategy for PWRNPS to address its nonlinearity problem in multivariable control within a wide range of conditions, in which the coupling effect between the average coolant temperature and the steam turbine power is considered. Given the insufficient understanding of the nonlinearity of PWRNPS in previous work, an in-depth investigation in this regard is performed to support the control design, in which the nonlinearity of PWRNPS at the system level is analyzed both on quantitative and qualitative aspects for the first time. Considering the captured uneven nonlinearity distribution of PWRNPS, a new approach integrating hierarchical clustering with gap metric is proposed to guide the decomposition of the operating space of PWRNPS systematically, based on which MMPC is designed to realize the nonlinearity control of PWRNPS under the large load variation. Three types of simulations demonstrate the effectiveness of the proposed methods.

Low-Carbon Optimal Scheduling Model for Peak Shaving Resources in Multi-Energy Power Systems Considering Large-Scale Access for Electric Vehicles

Aiming at the synergy between a system’s carbon emission reduction demand and the economy of peak shaving operation in the process of optimizing the flexible resource peaking unit portfolio of a multi-energy power system containing large-scale electric vehicles, this paper proposes a low-carbon optimal scheduling model for peak shaving resources in multi-energy power systems considering large-scale access for electric vehicles. Firstly, the charging and discharging characteristics of electric vehicles were studied, and a comprehensive cost model for electric vehicles, heat storage, and hydrogen storage was established. At the same time, the carbon emission characteristics of multi-energy power systems and their emission cost models under specific carbon trading mechanisms were established. Secondly, the change characteristics of the system’s carbon emissions were studied, and a carbon emission cost model of multi-energy power was established considering the carbon emission reduction demand of the system. Then, taking the carbon emission of the system and the peak regulating operation costs of traditional units, energy storage, and new energy unit as optimization objectives, the multi-energy power system peak regulation multi-objective optimization scheduling model was established, and NSGA-II was used to solve the scheduling model. Finally, based on a regional power grid data in Northeast China, the improved IEEE 30 node multi-energy power system peak shaving simulation model was built, and the simulation analysis verified the feasibility of the optimal scheduling model proposed in this paper.

Chronological operation simulation for power system with peak regulation ancillary service market in China: Formulation, solving, and validation

Peak regulation ancillary service market (PRSM) is an emerging and effective mechanism for renewable energy accommodation in China, especially under Carbon Neutral. To comprehensively analyze its long-term impact on the power system, a chronological operation simulation (COS) model is proposed. Besides, the computational-burden balancing technique based on the time-domain decomposition is developed to improve the computational efficiency of COS. The real-world northeast China power system with PRSM has been analyzed. Case studies validate the simulation with published statistical data and demonstrate the accuracy. Besides, 52.16% computation time is reduced compared to the widely used rolling horizon method. The necessity of PRSM for renewable energy accommodation is verified through chronological simulation. The relationship between peak regulation costs and renewable energy penetration is also given, which demonstrates its applicability.

Carbon dioxide from oxy-fuel coal-fired power plant integrated green ammonia for urea synthesis: Process modeling, system analysis, and techno-economic evaluation

A high proportion of renewable energy integration will be one of the significant characteristics of future electric power grid, and power-to-NH3 technologies will become an important approach for inter-quarter long-term energy storage. Meanwhile, as an important method of peak regulation in power systems, coal-based power plant is necessary to implement CO2 capture. This study constructed a novel urea synthesis process based on renewable energy, including water electrolyzers, air separation, green ammonia, oxy-fuel combustion power plant, CO2 purification, and urea synthesis units. Coal- and natural gas-based urea synthesis processes were selected as the contrasting processes.The results showed that CO2 emissions from the novel process proposed in this work were 0.124–0.174 t CO2/t urea, which was much lower than that of the coal and natural gas-based urea processes (1.4 and 0.8 t CO2/t urea, respectively). In the current technical economy (with coal and natural gas prices of 450 CNY/t and 1.7 CNY/m3, respectively), the production cost of this new process was about 1.0–2.2 times higher than that of traditional processes. When the carbon tax values were 121 and 371 CNY/t, the total production costs of the natural gas- and coal-based urea process would exceed that of the new process, respectively. This study will provide a promising method for urea production based on renewable energy to realize CO2 utilization from coal-fired power plant.

Strategic behavior modeling and energy management for electric-thermal-carbon-natural gas integrated energy system considering ancillary service

Carbon capture system (CCS) and power to gas (P2G), as key low-carbon technologies, have obtained the widespread attention. Thus, it is necessary to explore the operation pattern and benefits of CCS and P2G. This paper investigates their roles in the electric-thermal-carbon-natural gas integrated energy system (IES) and focuses on the multi-market participation and multi-energy management. IES participates in the electricity, carbon trading, and natural gas markets. In the electricity market, IES can provide two types of ancillary services: deep peak regulation and frequency regulation services. Then, owing to the small scale of the ancillary service market, the strategic behaviors of IES are discussed. A bi-level model is established to deal with the complex couplings in the peak regulation market. IES's strategic behavior in the frequency regulation market is depicted by the price quota curve. Next, a solution method based on the particle swarm optimization algorithm is proposed to deal with the nonconvexity of the lower-level model. Finally, case studies show that the strategic behavior modeling in this paper can increase the profits of the deep peak regulation and the frequency regulation services. When IES is equipped with CCS and P2G, the operation cost decreases by 16.6%.

Flexibility-enhanced strategies of generations to improve the integration of renewable energy considering uncertainties

Power systems with a high proportion of renewable energy are confronted with immense challenges and a huge amount of renewable energy has been curtailed. To promote the integration of renewable energy, this paper focus on exploiting the generation flexibilities. For hydropower units, a high-resolution dynamic regulation model of reservoir volume is proposed, in which the real-time situation of inflow is considered. The reservoir’s flexibility which is hindered by the static flood water limit level (FLWL) is exploited by this method. For thermal units, a deep peak regulation of thermal is constructed to enhance the system’s flexibility. Furthermore, considering the uncertainties of renewable energy, a chance-constrained reservoir flexible model influenced by random reservoir inflow and a chance-constrained spinning reserve and power flow model influenced by the wind uncertainty are developed. Based on those models and their coordination, an optimal dispatching model considering generation flexibilities and uncertainties is proposed. Simulation results on the IEEE 30-bus system show that the proposed model with multiple flexible resources can increase wind power penetration by 10% and save half of the cost compared with the conventional dispatching methods. These results demonstrated the efficiency of the proposed method whether the integration of renewable energy or the saving of operation costs.

Optimal scheduling of thermal-photovoltaic power generation system considering carbon emission

Increasing the proportion of clean energy is an inevitable trend of high-quality energy development. While enjoying the benefits of clean energy, traditional fossil energy enterprises face great challenges. In this study, coal power and photovoltaic power are selected as examples for analysis. Considering coal consumption, carbon emission, unit ramp conditions and photovoltaic abandonment, a comprehensive income model containing thermal power and photovoltaic power generation is proposed. The objective function of maximizing earnings is established and solved by genetic algorithm. The results show that the overall benefit will increase if the proportion of photovoltaic does not increase blindly. The unreasonable ratio of thermal and photovoltaic power and the imbalance of supply and consumption will lead to the decline of economic benefits and the increase of coal consumption and carbon emission. In the case of a high proportion of clean energy penetration, on the one hand, traditional power plants should be responsible for ensuring the instability of electricity output when the photovoltaic is weak, on the other hand, they need to give up some of the supply proportion when clean power is at a high output state. Therefore, while increasing the proportion of clean energy, it is necessary to improve the speed of load adjustment, the depth of peak regulation and the efficiency of low load work in traditional power plants to ensure the healthy development of new energy.

Investigation on the Aerodynamic Performance of High-Pressure Cylinders under the Mode of Regulating Steam Supply with an Intermedium-Pressure Adjustment Valve

In order to investigate the operational performance of the high-pressure (HP) cylinder towards a 300 MW boiler unit, the three-dimensional calculation method was applied to simulate the aerodynamic flow characteristics of the last five stage cylinder blades, including limiting streamline, isentropic efficiency along the blade height, and relative work amounts. Simulation results show that with the back pressure controlled at 3.4 MPa and the inlet steam flow decreasing to a certain extent, the flow state of the last three stage blades begins to deteriorate. When the back pressure continues to drop, the flow state becomes unstable at the blade root and the blade tip on the suction surface of the rotor blade, while the flow state is stable on the pressure surface of the rotor blade and the surface of the stationary blade. If the flow rate decreases, the flow separation area on the suction surface of the moving blade decreases, and the flow performance can be improved. With the decrease in the boiler load, the total-total isentropic efficiency for the last three stage blades of the HP cylinder gradually decreases. In addition, the isentropic efficiency at the blade root and blade tip is lower than that for the blade body, which is closely related to the flow separation phenomenon of the last three stages of moving blades at the root and tip. In the working condition of the intermedium-pressure adjustment valve, since the temperature variation is within a limited range, operational safety can be guaranteed under the design blade frequency and strength. This paper provides the upper and lower limits of the exhaust gas pressure for the HP cylinder under the intermedium-pressure adjustment valve participation mode concerning various working conditions as well as corresponding constraints. This is conducive to guiding the design and safe operation of the intermedium-pressure adjustment valve. Through adjusting the intermedium-pressure adjustment valve, a specific supply capacity of the unit can also be satisfied at the lower load, which is beneficial to the deep peak regulation of the unit.