Renewable Energy Accommodation Research Articles

Energy saving and flexibility analysis of combined heat and power systems integrating heat-power decoupling technologies in renewable energy accommodation

World energy is undergoing a decarbonization transformation with the rapid growth of renewable energy. Combined heat and power, with its abundant stock and efficient characteristics, can support decarbonization. This study focuses on enhancing the energy saving and flexibility of combined heat and power systems decoupled by the vapor compression heat pump and electric boiler. The characteristics of the governing valves and the governing stage are confirmed as key factors influencing energy saving. A general modeling method is proposed to accurately determine the characteristics of the governing valves and the governing stage. The valve-point effect is obtained from the formation mechanism. With a reference case, applying the heat-power decoupling technologies results in a maximum reduction of power load of up to 70 MW, greatly enhancing the operation flexibility. The fuel saving per unit peak shaving ranges from 0.1355 kg·kWh−1 to 0.1382 kg·kWh−1 in the system decoupled by the electric boiler and from 0.2284 kg·kWh−1 to 0.2481 kg·kWh−1 in the system decoupled by the vapor compression heat pump. The maximal variation ranges of fuel saving per unit peak shaving, with and without the valve-point effect, are 0.0197 kg·kWh−1 and 0.0055 kg·kWh−1, respectively. After considering the valve-point effect, the range of fuel saving per unit peak shaving is tripled. Additionally, there are higher energy-saving regions near valve points. It is recommended to operate the decoupling systems under these working conditions, with governing valves opened at an appropriate opening degree. The energy-saving potential of these systems can be further exploited by accommodating the valve-point effect in the integration of renewable energy.

Enhancing Renewable Energy Accommodation by Coupling Integration Optimization with Flexible Retrofitted Thermal Power Plant

Thermal power units (TPUs) play a crucial role in accommodating the high penetration of renewable energy sources (RESs) like wind turbines (WTs) and photovoltaics (PVs). This paper proposes an evaluation framework to quantitatively analyze the flexibility potential of retrofitted TPUs in enhancing the accommodate capability of RESs through coupling integration and optimal scheduling. Firstly, the coordination framework for coupling TPUs with RESs is outlined, including a comprehensive analysis of benefits and implementation strategies. Secondly, an annual optimal scheduling model for TPUs and RESs is developed, incorporating deep peak regulation services, ladder-type constraints for retrofitted TPUs, and their operational characteristics before and after the coupling integration. Thirdly, indices to evaluate RES accommodation levels and TPU regulation capacities are proposed to quantify the performance of power sources. Finally, a real-world case study is conducted to demonstrate that integrating retrofitted TPUs with RESs through coupling significantly enhances RES utilization by 3.6% and boosts TPUs’ downward regulation capabilities by 32%.

Investigation of solar accommodation via storage configuration in district heating system based on granularity analysis

In the pursuit of achieving carbon neutrality, it is essential to advance the broad accommodation of renewable energy for the realization of a low-carbon district heating system (DHS). The heat storage configuration serves as an effective assurance for facilitating the accommodation of renewable energy in DHS. This paper analyzes the impact of heat storage configuration on the accommodation of solar energy in DHS, employing a granularity analysis method. This study proposes an optimization method for determining the maximum solar energy accommodation rate in solar-assisted DHSs using the granularity analysis method. Furthermore, this study optimizes the heat storage capacity configuration to facilitate varying ratios of solar energy accommodation. Finally, a technical-economic analysis of the solar-assisted DHS is performed in the context of rising energy cost. The case study reveals that greater granularity leads to a higher solar energy accommodation rate. Specifically, when the granularity increases from 0.43% to 22.22%, the solar energy accommodation rate increases by 8.01%. Moreover, the heat storage capacity configuration increases with the increase of the solar energy accommodation rate, while the heat storage capacity required to accommodate per unit of solar energy decreases. Additionally, as the granularity increases from 0.43% to 22.22%, the heat storage capacity configuration under the same solar energy accommodation ratio increases gradually. This study explores the potential of granularity-based heat storage configuration optimization in promoting solar energy accommodation and achieving the economy of DHS, which is of great significance for the decarbonization of DHS.

Grey-box model-based demand side management for rooftop PV and air conditioning systems in public buildings using PSO algorithm

As the major power consumer in buildings, Heating, ventilation and air conditioning (HVAC) systems can significantly contribute to the economical operation of the power system during the peak periods in summer or winter, by flexibly responding to the grid demands. Indoor air temperature set-point resetting is a typical passive demand response (DR) control strategy that have potentials to be widely implemented by inverter air conditioners in residential buildings. However, in the existing small and medium-size public buildings, the compressor motor of HVAC systems mostly does not allow for stepless speed changes. This means that the indoor temperature set-point resetting strategy may results in frequent on/off switching of the chiller plant, leading to a compromised lifespan. Although rooftop photovoltaic systems (PVs) are expected to be widely adopted in new buildings, their potential for peak load shaving or valley load filling has received limited investigation. This study proposes a grey-box model-based demand side management (DSM) method for variable air volume systems (VAVs) and rooftop PVs in public buildings. The DSM method utilizes the particle swarm optimization (PSO) algorithm to determine optimal indoor air temperature and/or evaporator outlet water temperature set-point schedules for the VAVs, as well as optimal charging/discharging schedules for the storage battery of rooftop PVs. This approach aims to reduce the peak power demands of the integrated system without causing severe thermal discomfort, increasing chiller mechanical costs (refers to frequent on/off switching of the chiller plant in this paper), or overcharging/over-discharging of storage batteries. The results of case studies demonstrate that the strategy of co-resetting indoor air temperature – evaporator outlet water temperature set-points outperforms the indoor air temperature set-point resetting strategy in terms of both peak power demands reduction and mechanical cost saving when applied to the VAVs in low performance buildings. In comparison, for the VAVs installed in normal buildings, both the strategies yield significant decreases in peak power demands and mechanical cost by fully utilizing the thermal capacity of the building internal thermal mass. Furthermore, by implementing the DSM method for rooftop PVs, the integrated system can achieve zero or near-zero net power demands during the DR periods. The proposed DSM method can also be utilized in valley load filling programs to facilitate the accommodation of renewable energy.

A day-ahead coordinated scheduling strategy for source storage and load considering demand response and lines loss

Day-ahead scheduling strategy is an effective way to improve the renewable energy accommodation. To increase the renewable energy accommodation in the regional power grids, reduce the total costs of the power system, and improve the supply reliability of the power system, this research suggests a multi-time-scale “source-storage-load” coordinated dispatching strategy that considers the distribution and characteristics of pumped energy storage and loss of the network. Taking the wind curtailment penalty costs, the system operating costs, and the load loss penalty costs as the objective functions, a day-ahead coordinated scheduling strategy for source storage and load considering demand response and lines loss is established. Finally, the commercial software package CPLEX is called through the MATLAB platform to complete the optimization of mixed integer programming. Simulation results shows that the proposed scheduling strategy could build the power generation plant, effectively adjust the output power of pumped storage, and regulate the assumption of translationable load and transferable load.

Performance study of 660 MW coal-fired power plant coupled transcritical carbon dioxide energy storage cycle: Sensitivity and dynamic characteristic analysis

As the demand of modern society for electrical energy continues to rise, the combustion of fossil fuels has led to deterioration of global climate due to the emission of greenhouse gases. The introduction of zero-carbon goal has spurred rapid development of renewable energy. However, the intermittent nature of it has revealed the growing issue of insufficient capacity in existing power systems. To tackle this challenge and enhance the adaptability of power systems, improving the flexibility of coal-fired power plants has become a critical objective.In this work, a novel solution is proposed to address the lack of renewable energy accommodation capacity. It is the method of coupling transcritical carbon dioxide (T-CO2) energy storage cycle with the 660 MW coal-fired power plant (CFPP), using energy storage process to further reduce unit load and energy release process to increase it. The results show that, under the design operating parameters, CFPP achieves load increase and decrease rates of 70.32 MW/min and 35.04 MW/min, respectively. Moreover, at a thermal storage temperature of 353.1 K, T-CO2 energy storage cycle achieves a round-trip efficiency of 61.37% and an energy storage density of 0.989 kWh/m3. The results indicate that the combined system exhibits excellent performance, particularly under relatively lower thermal source temperature.

Novel performance assessment method for superheated steam control of a coal-fired power plant under renewable energy accommodation condition

For the large-scale accommodation of intermittent renewable energy, coal-fired power plants operate under rapid load change condition so that the superheated steam temperature exhibits highly non-linear characteristics. This study proposed novel solution procedures for non-stationary and operating data to quantitively assess the performance of secondary superheated steam control. De-mean and difference operations were employed for non-stationary data to stabilize the data sequences. Auto regressive models were then built to estimate the time delay such that the assessment index of the minimum variances can be calculated. The results show that the proposed auto regressive model has the highest accuracy and its order is relatively stable at three, when the sampling period is approximately 20–34 s. Taking into account the variations in secondary superheated steam temperature, which amounted to ±8 ℃ and ±2.5 ℃ during low-load and high-load conditions respectively, the assessment index of minimum variance under the low-load operation was 0.495, which was 41 % lower than that under the high-load operation. This indicates that the higher the load operates, the better the randomness performance of the superheated steam control is. These findings may help optimize the superheated steam control strategies in coal-fired power plants.

Optimal Allocation Stochastic Model of Distributed Generation Considering Demand Response

Demand response (DR) can improve the accommodation of renewable energy and further affect the distributed generation (DG) allocation strategy. In this context, this paper proposes a stochastic optimal allocation model of DG, considering DR. Firstly, to address the uncertainty of wind and solar power outputs, a large number of scenarios of wind and solar power are generated based on the scenario method, which are then clustered into 10 typical scenarios by the k-means method. Secondly, with the goal of maximizing the total cost, the DR cost and corresponding constraints are introduced. Then, the stochastic planning model for DG is established, where the planning level aims to minimize the investment cost while the operation level minimizes the total operation expectation cost. For the non-linear term in the DR cost and power flow constraint, the Taylor expansion method and second-order conic relaxation method are both adopted to transform the original mixed-integer non-linear model to the mixed-integer second-order conic planning model. Finally, the whole planning model for DG is solved by CPLEX 12.6.0. The results show that DR can reduce the total cost and improve the accommodation of renewable energy in the DG planning process, which should be paid more attention to in the DG planning model.

Fault mitigation mechanism to pave the way to accommodate over 90% renewable energy in electric power systems

Renewable energy plays a key role in reducing carbon emissions. The electric power system is widely considered a platform to accommodate renewable energy because of the high efficiency in transmitting energy through electricity tie lines. However, most renewable energy generation is weather-dependent, bringing difficulties in balancing generations and electricity demands. There are two major solutions to address the imbalance problem. The first is to balance the fluctuation of renewable energy in time scales by deploying energy storage, but with huge costs and security risks. The other is to balance the fluctuation in spatial scales by enhancing the interconnection among electric power systems, which however results in a sharp increase in the fault current in many countries. The high level of fault current threatens the security of power system. In other words, the overrating fault current of electric power systems is one of the key bottlenecks for carbon neutrality. To mitigate the high-level fault current because of the interconnection, we propose a novel mechanism, namely fault current release. It achieves new functionalities with proven equipment at low cost and will create no impact on the electric power systems in normal operation. The simulation selected six representative electric power systems, including those in the USA, the UK, China, Brazil, Nigeria and IEEE reliable test system. Results show that the fault current release method enables an increase of renewable energy accommodation from 19.2% ∼ 47.2% to over 90%, much higher than the existing methods can do. Correspondingly, the emissions of CO2, NOX and SOX in these systems decrease by 67.84% ∼ 88.07%, 61.17% ∼ 88.24% and 58.25% ∼ 88.33%, respectively. The proposed method is also economical in that it can accommodate renewable energy at about one-tenth the cost of energy storage. Our findings hence suggest a promising way to build a carbon-free energy system.

A novel 4-level joint optimal dispatch for demand response of data centers with district autonomy realization

Data centers (DCs) are gradually becoming one of the world's largest energy consumers. Owing to their spatial-temporal transferable characteristics, DCs show great potential in demand response (DR) programs. Considering the difference in task delay sensitivity (TDS), an elaborate DR model of DCs is proposed in this work. Furthermore, to ensure the TDS requirement and relieve the data transmission pressure, TDS-based unit migration cost matrices are designed in this work. Given that most DR dispatch for DCs is conducted relying on a central dispatching center, which imposes critical requests for the reliability of the dispatching system. In this work, we establish a novel 4-level joint optimal dispatch model for the DC operator, where dispatching centers installed at each level integrate district information from bottom to top and conduct optimal task dispatch from top to bottom. Moreover, an innovative dispatch autonomy mechanism is designed to realize district autonomy in the case of dispatching center breakdowns. Finally, simulation results prove that the proposed 4-level joint optimal dispatch model has a positive effect on enhancing renewable energy accommodation and improving the economic benefits of the DC operator. More specifically, by applying the proposed 4-level joint optimal dispatch model, the DCO's total cost is decreased by 32.25% in comparison with the result of no DR participation. It also proves that our designed dispatch autonomy mechanism can achieve district autonomy under the breakdown of dispatching centers at any level.

The optimization analysis of multi-type demand-side flexibility resources for renewable energy accommodation in electrical power systems

The significant increase in the proportion of renewable energy access has led to a burgeoning and urgent demand for flexible resources within the power system, and the prominence of flexible resources on the demand side has been underscored. The existing research on demand-side response primarily focuses on addressing peak load challenges, while the optimization of demand response for accommodating renewable energy remains inadequate. Considering this issue, the present study initially examines the flexibility mechanism offered by demand-side resources and proposes a calculation approach for determining the adjustable capacity of demand-side flexible resources. Furthermore, considering the limitations of the load that can be shifted, transferred, and curtailed, we develop a collaborative optimization operational model for accommodating renewable energy with multiple types of demand response. Finally, based on the provincial power grid example, the simulation study verifies the efficacy of a multi-type demand response. The results demonstrate that shiftable and transferable loads can effectively facilitate the accommodation of new energy, and their optimal allocation capacity ratios are 5.8% and 2.3%, respectively.

Optimal dispatch of distributed renewable energy and energy storage systems via optimal configuration of mobile edge computing

AbstractThe access of distributed units leads to the rapid increase of power network information services, which brings great problems to the centralized dispatch of power system. To improve the efficiency of data processing and the flexibility of each unit dispatching, first, the areas are divided according to the load characteristics. An operating framework of distributed power system is presented based on offload strategy of mobile edge computing (MEC) and optimal allocation of computational quantity. Second, a novel hierarchical dispatching model is established based on the optimal configuration of MEC. In each area, the upper model takes the total energy consumption of the MEC server and the operating cost of the power system as the objective to calculate the output of each distributed unit and the computational quantity under the optimal offload strategy. The lower model ensures the complete accommodation of renewable energy and the optimal economic operation of the whole area through the introduction of electric vehicles (EVs) and secondary operation of energy storage systems under the constraint of MEC server computing delay. Third, the screening rules of the MEC system computing mode and the simulation strategies of EVs in different areas are proposed. Finally, the effectiveness and superiority of the model are verified by simulation experiments.

Multi-Objective Capacity Optimization of Grid-Connected Wind–Pumped Hydro Storage Hybrid Systems Considering Variable-Speed Operation

The coordination of pumped storage and renewable energy is regarded as a promising avenue for renewable energy accommodation. Considering wind power output uncertainties, a collaborative capacity optimization method for wind–pumped hydro storage hybrid systems is proposed in this work. Firstly, considering the fluctuation of wind power generation caused by the natural seasonal weather and inherent uncertainties of wind power outputs, a combined method based on the generative adversarial network and K-means clustering algorithm is presented to construct wind power output scenarios. Then, a multi-objective wind–pumped storage system capacity optimization model is established with three objectives consisting of minimizing the levelized cost of energy, minimizing the net load peak–valley difference of regional power grids, and minimizing the power output deviation of hybrid systems. An inner and outer nested algorithm is proposed to obtain the Pareto frontiers based on the strength of the Pareto evolutionary algorithm II. Finally, the complementarity of wind power and pumped storage is illustrated through an analysis of numerical examples, and the advantages of variable-speed pumped storage in complementary operation with wind power over fixed-speed units are verified.

Control strategies for active distribution networks based on soft open point

The application of SOP instead of traditional tie line switches in distribution networks can im-prove the controllability, flexibility, and renewable energy accommodation of the distribution grids. Compared with traditional tie line switches, SOP have high costs and large land occupation. When power transfer is not considered, the location and capacity of SOP can be optimized through planning algorithms to reduce the overall costs. However, the main function of tie line switches is power transfer. If SOP as alternative devices do not meet this requirement, their ex-tensive application will be restricted. Therefore, this paper designs the direct current volt-age-power smooth switching control strategy, phase angle pre-synchronization control strategy, and voltage support control strategy based on the parallel structure of SOP and tie line switches to ensure stable operation during the power transfer process, through this complete control strategy, smooth switching can be achieved to keep the DC voltage stable during the switching process, effectively suppress the impulse current and maximize the utilization of SOP capacity Simulations on the SIMULINK plat-form verify the feasibility of the control strategies.

Optimal Scheduling Framework of Integrated Energy System Based on Carbon Emission in Electricity Spot Market

Micro coal-fired units (MCFU) and combined heat and power plants (CHP) in integrated energy system (IES) will emit a large amount of carbon dioxide when providing loads to customers, which will lead to higher operating costs of IES. To solve this challenge, an optimal dispatch model of power-to-gas (P2G) and methane reactor (MR) considering the reward and punishment costs based on carbon emission trading mechanism is proposed, to reduce carbon emissions of IES and enhance the accommodation of renewable energy (RE). Subsequently, considering the uncertainty of RE, a combination optimization method for MCFU and CHP units is developed based on the Lagrange multiplier method. Finally, considering the mechanism of electricity spot market (ESM), a transaction strategy for IES participating in ESM is proposed to further enhance the accommodation of RE. The effectiveness of the proposed framework is demonstrated through simulations.

Assessment of machine learning models and conventional correlations for predicting heat transfer coefficient of liquid hydrogen during flow boiling

Liquid hydrogen has attracted widespread attention due to industrial decarbonization as an excellent carrier of renewable energy accommodation. Accurate prediction of the heat transfer coefficient during flow boiling has become a fundamental prerequisite for enhancing the design rationality and safety of hydrogen devices since a slight heat leak will trigger flow boiling. A novel selection standard of the dataset for liquid hydrogen flow boiling is proposed, and an experimental database of liquid hydrogen is compiled, including 923 data points from 5 sources. The prediction performance of eight conventional empirical correlations and six machine learning models for liquid hydrogen flow boiling is compared and evaluated. Results show that the prediction ability of machine learning models for the heat transfer coefficient of liquid hydrogen flow boiling is much higher than that of traditional correlations. Fang correlation is recommended to use as the calculation benchmark due to simplicity and reliability if the inlet condition is obscure. The Extra tree model shows the most excellent evaluating performance among the six machine learning models with a mean absolute deviation of 11.44% and a fairly superior R2 of 0.9543. The Froude number Fr exhibits an essential impact according to the feature importance analysis, which may need to be taken into account when developing new empirical correlations. An improved correlation for the flow boiling of liquid hydrogen is provided based on the Fang correlation, achieving a mean absolute deviation of 18.61% and a mean relative deviation of 16.49%. The results of this paper can offer some theoretical guidance for the design of a liquid hydrogen apparatus.

Evaluation of Renewable Energy Accommodation Capacity Considering Multiple Flexibility Resources of Source-Load-Storage

The utilization of multiple flexibility resources (MFR), such as source-load-storage (SLS-sides), plays a vital role in promoting the integration of large-scale grid-connected renewable energy (RE). Analyzing the renewable energy accommodation capacity (REAC) and assessing the impact of flexibility resources on enhancing REAC is vital for the coordinated development of SLS sides. This study focuses on the technical mechanisms of thermal power (TP) deep peak regulation, load demand response (DR), and energy storage (ES). Considering the operational constraints of these MFR, an evaluation model for REAC is established. The model is a multi-objective optimization framework that encompasses the maximized installed capacity of RE while minimizing the comprehensive expenses of the power system. A hierarchical decoupling solution method is used to solve the evaluation model, obtaining the Pareto optimal solution set for RE installed capacity and power system operating cost. Subsequently, a fuzzy membership function is applied to select a compromise solution, quantitatively characterizing the REAC. The case study results demonstrate that by effectively coordinating the “SLS-sides” MFR, the REAC of the IEEE-RTS96 system can be improved from 1, 758 MW to 2, 731 MW, indicating a remarkable enhancement of 55.35%.

Transmission and distribution network-constrained large-scale demand response based on locational customer directrix load for accommodating renewable energy

Large-scale demand response (DR) is a promising solution to mitigate the problem of renewable energy (RE) curtailment with the rising proliferation of RE in both the transmission system (TS) and distribution system (DS). However, the current DR schemes face significant limitations when applied at a large scale, and the lack of consideration of TS and DS networks stymies the potential of large-scale DR in facilitating RE accommodation. To address these gaps, this paper proposes a novel hierarchical DR scheme based on locational customer directrix load (LCDL) that takes into account both TS and DS, involving interactions among tri-layer entities: the transmission system operator, distribution system operators, and customers. Firstly, the concept of LCDLs is proposed to characterize the desired load profiles at various locations, considering the constraints of both TS and DS networks. Subsequently, transmission-level and distribution-level LCDLs are formulated respectively and leveraged to induce the load reshaping of flexible resources at pertinent locations, thereby unlocking the deliverable flexibilities over TS and DS. Furthermore, the collaborative interaction among the three layers is depicted by a two-loop Stackelberg game, and a distributed algorithm is presented to achieve an equilibrium solution without compromising the privacy of the respective entities. Case studies testify that the proposed DR scheme significantly enhances the capacity to accommodate RE in both TS and DS, maintains economic balance in the trading process of DR services, and benefits all involved entities. Conducted on a practical city power grid with a substantial share of RE and DR customers, the simulation test validates the scalability of the proposed DR scheme and results show that with a 10% increment of DR customers in one DS, the RE curtailment rate could be reduced by 3% and 15% for TS and DS, respectively.

A stochastic hierarchical optimization and revenue allocation approach for multi-regional integrated energy systems based on cooperative games

The interconnection of multiple regional integrated energy systems (RIES) can effectively enhance the low-carbon and flexible operation capabilities of RIES, but the uncertainty and multi-energy interaction of the system pose challenges to the stable operation of RIES. Therefore, a stochastic hierarchical optimization and revenue allocation approach is proposed to optimize the operational strategy of multi-RIES with multi-operator participation, multi-energy interactions, and multiple uncertainties. Firstly, agent-based modeling, Latin hypercube sampling and simultaneous backward reduction based on the Wasserstein metric are proposed to capture the power-side and load-side uncertainties. Secondly, based on the Nash bargaining game and the alternating direction method of multipliers algorithm, multi-RIES peer-to-peer transactions of electricity, thermal, and natural gas are optimized through energy cooperation and scheduling strategies. Subsequently, a Nash-Harsanyi bargaining game revenue allocation method that considers both fairness and renewable energy accommodation is proposed to ensure the stable operation of the alliance. Finally, simulations are conducted on a multi-RIES in northern China, demonstrating that the proposed model and approach can achieve inter-grid flexible resource complementarity, improve RIES economics, increase local renewable energy accommodation rate, and reduce carbon emissions.

Opinions on power grid infrastructure investments for renewable energy accommodation in China

Opinions on power grid infrastructure investments for renewable energy accommodation in China