Integrated energy systems (IESs) are widely used for deep coupling of heterogeneous energy sources, including electricity, gas, hydrogen, heat, carbon-based fuels, and renewable energy. The traditional “energy” perspective is no longer suitable for comprehensive energy efficiency evaluation of IESs, hence the urgent need for a novel measurement standard. For dual considerations of “quantity” and “quality,” exergy has been introduced into IESs in recent years and fundamentally transformed our concepts of energy utilization, playing an important guiding role in the construction of low-carbon and high-efficiency IESs in the future. First, based on the origin and development of exergy, the basic concepts and integration trends towards multi-energy systems are introduced. Subsequently, the theoretical model and analysis method of exergy in IESs are introduced from the perspectives of black and white boxes, and their characteristics and applicability are evaluated. Next, an overview of the integration of IES optimal planning and market economy in recent years is provided, and its guiding role in key technologies, such as IES optimal configuration, real-time scheduling, production process economics analysis, and heterogeneous energy pricing, is analyzed. Finally, a summary and future prospects are presented.
As broad application of traditional physical energy storage equipment is difficult due to high construction costs, the low-carbon economic dispatch model of an integrated energy system considering electric-thermal equivalent virtual energy storage is proposed in this paper. First, the heat load is adjusted using the thermal comfort index of residential users and combined with combined heat and power (CHP) units to form a thermally equivalent virtual energy storage. Second, the carbon capture equipment is regarded as an adjustable load, and participates in peak shaving and valley filling as an equivalent virtual energy storage with a demand-side response. In addition, the carbon capture equipment operates at the maximum power during periods of low load, which can effectively reduce the CO2 emissions of the CHP units. Finally, a model is established with the goal of minimizing the sum of energy purchase costs, wind curtailment costs, CO2 sequestration costs, carbon transaction costs, and demand response compensation costs. The simulation results of the example show that, considering the synergy of thermal equivalent virtual energy storage, demand response, and carbon capture equipment simultaneously; the overall wind energy penetration rate of the system is improved, carbon emissions are reduced, and the economy of integrated energy system is improved.
With the development of a low-carbon economy, the coupling degree of electric thermal gas systems is continuously increasing. The operation mode of traditional energy supply systems using electric heating separation mode and hierarchical dispatching of transmission and distribution networks has made it difficult to mine entire network resources and realize a global optimal operation strategy. To increase the consumption of new energy and solve the multi-energy current coupling system, this study proposes a layered optimization operation strategy for an electric-thermal multi-energy current coupling system with a concentrated solar power (CSP) plant and a carbon trading mechanism. The upper layer is the solution layer of a multi-energy coupled flow system. To solve the matrix values of a complex multi-energy coupled flow system, Newton's, improved Newton's, and improved second-order cone collaborative solution methods are proposed. The lower layer is a multi-energy flow optimization layer. According to the system solution values obtained from the upper layer, the lower-layer optimization aims to minimize the total user cost. A carbon trading mechanism model was introduced to optimize the time sequence output of an electric thermal unit under different scenarios, and a mixed-integer linear programming method was adopted. Finally, its validity was verified through simulation. The results show that this method can optimize the operation of the system and improve its accuracy and rapidity. In addition, the “carbon trading and CSP power station” method can better constrain the carbon emission of multi-energy flow coupling systems, reduce the energy pressure of the device, and improve the economy of the total cost of the user.
To address the impact of decision makers’ risk attitudes (DMRAs) and uncertainties on dispatch strategies in community-integrated energy systems, a multi-objective dispatch model of a community virtual power plant (CVPP) is proposed. First, a novel CVPP model that considers DMRA and a multi-objective economics-energy-environment satisfaction model was developed. The information gap decision theory (IGDT) model is then improved considering uncertainties of renewable energy, loads, and DMRA. Third, considering DMRA, an improved VIKOR method was proposed under self-confident double-hierarchy linguistic preference relations. Finally, the effectiveness of the proposed model was validated regarding a multi-scenario example of a residential area. The results indicate the following: 1) The novel CVPP provides realistic scheduling strategies based on the DMRA. 2) After implementing demand response, the resident cost and net carbon emissions are reduced by 9% and 91%, respectively. In addition, the energy supplier profit and renewable energy utilization rate are increased. The constructed IGDT model also improves multiple objectives. 3) The improved uncertainty and deviation factors of the IGDT model allow diverse scheduling strategies. Simultaneously, the improved VIKOR method provides a new way for decision makers to select strategies. This model serves as a guide for selecting scheduling strategies and encourages the use of renewable energy sources.
Greenhouse environmental systems and photovoltaic power plants in modernized agricultural parks are prone to “source-load peaking” operation problems. To address this issue, this study proposes a two-layer optimization model for energy management of the agricultural park micro energy network (APMEN) participating in distribution market clearing, considering the crop growth characteristics to achieve rapid growth of crops and efficient use of multiple types of energy. The upper-layer model calculates the optimal energy management strategy for the greenhouse environmental system of the agricultural park, with the objective function of minimizing the operating cost of the APMEN, considering the accessory market tariff and photovoltaic power generation. The lower-layer model corresponds to the distribution market-clearing model, which aims to achieve optimal power allocation in the distribution network. Furthermore, the two-layer model is solved using the adaptive alternating direction multiplier method. Finally, the IEEE 12-node distribution system and 66-node Inner Mongolia Autonomous County rural distribution system were used as examples to verify the effectiveness of the proposed model and method. The results show that the model can effectively reduce the energy cost of agricultural parks by 23.2% and the power purchase cost of distribution network users by 6.2%.
To support the integration of intermittent offshore wind power, a hierarchical control method based on the coordination of turbine generators and energy storage was developed for power grids of offshore oil and gas platforms. First, the multi-energy coupling characteristics and structures of offshore oil and gas platform energy systems were analyzed. Subsequently, a hierarchical control strategy was devised based on day-ahead scheduling and intraday horizon dispatch to smooth uncertainties at hourly level. Furthermore, a saturated filter-based power dispatch strategy was developed to coordinate turbine generators and energy storage to smooth the uncertainties at minute and second levels. Finally, the proposed strategy was tested using data from a real offshore oil and gas platform energy system. The results show that the proposed strategy can reduce the operating cost while supporting the integration of wind power.
Model predictive control (MPC) is widely used in modular multilevel converters (MMC) because of its advantages such as fast dynamic response, low output current harmonic content and easy for multi-objective control. However, when connected to an unbalanced power grid, the energy asymmetry of the upper and lower arms of MMC causes the fundamental frequency circulating current fluctuation, and the zero-sequence with double fundamental frequency and higher frequency current flows through the DC bus, leading to the power fluctuation of DC bus, which is reflected in the current. In this paper, the unbalanced current is decomposed into positive sequence, negative sequence and zero sequence components, and a model predictive control method is proposed to suppress the negative sequence and zero sequence components of the circulating current by using a composite controller based on the phase leg energy, so as to realize the comprehensive control of the AC output current, the circulating current and the submodule capacitor voltage under unbalanced conditions. This method can not only effectively suppress the circulating current fluctuations under unbalanced conditions, but also suppress the fluctuations of the DC bus. The MMC model is built by MATLAB/Simulink for simulation analysis, and the correctness and effectiveness of the proposed method are verified by the experimental platform.
An input-series-output-parallel (ISOP)-type DC transformer using a dual-active bridge (DAB) DC-DC converter as the power unit is a key component of a DC distribution network. To improve the dynamic response speed and anti-interference ability of the system under input voltage pulsation and load mutation conditions, a model predictive control strategy based on the predictive correction method (PCM) is proposed that analyzes and establishes a mathematical model of the ISOP-DAB. The state-space average equation of the converter is derived, and the PCM is used to optimize the input voltage balance and the predictive control model of the output voltage. Furthermore, to effectively reduce the number of system sensors, a model predictive control strategy without load-current sensors is proposed. The proposed strategy effectively improves the dynamic response speed and anti-interference ability of the ISOP-DAB converter and ensures the balance of the input voltage for each module. Finally, a two-unit ISOP-DAB semi-physical simulation model was built in the RTDS, and the experimental results verified the correctness and effectiveness of the proposed control strategy.
The interaction of HVDC controllers may lead to the deterioration of subsequent commutation failure. Therefore, this study analyzes the mechanism of converter control interaction at each stage after a fault and designs a strategy to reduce the risk of subsequent commutation failure. According to the action logic of the inverter-side controller, the process of system recovery from fault occurrence to a steady state is divided into four stages. Based on the dynamic trajectory analysis of each stage, the influence of the inverter-side controller on the commutation failure recovery process is determined. The results showed that the secondary interaction process between the constant extinction angle control and constant DC current control on the inverter side, namely, the current deviation control stage, could easily cause subsequent commutation failure. An improved current deviation control strategy is proposed to improve the accuracy of the extinction angle state recognition by compensating for the current deviation during the secondary interaction of the controllers to suppress the subsequent commutation failure caused by improper controller interaction. Finally, based on the CIGRE HVDC benchmark model, an improved control strategy is tested under different operating conditions using the PSCAD/EMTDC simulation platform. The results showed that the analysis of the fault recovery process was accurate, and that the proposed subsequent commutation failure suppression strategy was effective.
The application of an active power filter (APF) at high voltage and large capacity requires the use of a modular multilevel converter structure (MMC). However, owing to the deviation of the system impedance and other parameters from the actual parameters, the predicted output value of the traditional model predictive control (MPC) loses its optimality, which ultimately affects the compensation effect of the MMC-APF. Therefore, this paper proposes an error feedback model predictive control strategy for MMC-APF. Combining the MPC and voltage sorting algorithms, the calculation amount of the switching state in each control period is reduced from
In this study, we explored the application of big data of power regarding social governance, which can aid decision-making for the government in digitally conducting social livelihood work. Specifically, elderly people who live alone currently lack effective technical identification means; thus, a method for analyzing the electricity consumption behavior of specific power users based on multidimensional load characteristic mining is proposed. First, based on the load curve, a power consumption behavior characteristic index was constructed, incorporating the mutual information value to add weight and reduce the subjectivity of the index setting. Simultaneously, combined with the convolutional block attention neural network model, the load data of elderly residents living alone and not living alone were extracted to obtain multidimensional load feature vectors that can represent the two types of residents’ power consumption behavior. Next, a β-cascaded forest model was employed for adaptive representation learning of vectors to solve the problem of sample imbalance in the classification process and improve the model recognition performance. Finally, a residential user in a community in Zhejiang Province was considered as an example to verify and monitor the electricity consumption behavior of elderly people living alone. The results prove that the method has good recognition performance on data with a small sample size and sample imbalance.
Currently, the ground-truth feature dataset obtained from coupling models of power system calculations and carbon emission flow (CEF) is insufficient and may not meet the demanding requirements for developing high-quality, precise, and timely electric-carbon coupling technology. This study proposes a novel generative method to produce a simulation-based dataset by incorporating generative adversarial network theory and the CEF model. The time-series electricity system state parameters and CEF characteristics are imported as learning samples to generate a learning module with intelligent fittings of the time-series CEF coupling associations. To address the training difficulties of generative networks, a transfer learning module is introduced to pretrain the CEF characteristics in small samples and transfer the trained parameters to the target domain to improve the efficiency of learning on the target task. An applicable generative model is obtained through a comparative study. Additionally, to address the lack of evaluation mechanisms for the generated data, a calculation-induced assessment method for the CEF data is proposed to verify and quantify the effectiveness of the generative transfer learning model. The proposed generative transfer learning framework is validated on IEEE 14-bus and 118-bus systems in a numerical study.
The energy characteristics of sub-synchronous oscillations in new power systems are closely related to operating conditions, control parameters, and the external environment. Therefore, the extraction of the energy characteristics of sub-synchronous oscillations and the identification of the dominant factors based on the measured data under different scenarios can effectively solve the sub-synchronous oscillation problems in practical engineering. In this study, we propose a data-driven method to identify the factors influencing sub-synchronous oscillation energy characteristics. First, it solves the modal mixing problem based on the variational modal decomposition to extract the accurate sub-synchronous oscillation modes; second, it derives the expression of sub-synchronous oscillation energy function based on the port energy and uses the modal extraction results to calculate the energy function; finally, it uses the subjective and objective weighting to establish an evaluation model to identify the dominant factors of sub-synchronous oscillation energy characteristics by considering the subjective and objective weights. The simulation results of the wind farm grid-connected sub-synchronous oscillations built based on the PSCAD/EMTDC platform verified the effectiveness of the proposed method.
With the "source-grid-load-storage integration" and large-scale integration of distributed photovoltaic (PV), the grid condition is gradually presenting as a weak grid. Furthermore, multiple inverters are connected to the end of the distribution network to form a complex impedance network that increases the risk of resonance. To investigate the resonance mechanism of a distributed PV system under a weak grid, the Norton model of the inverter, considering the current loop, capacitive current feedforward, and control delay, is derived. Then, considering the influence of the load at the point of common coupling (PCC) on the system impedance characteristics under a weak grid, an equivalent model of a grid-connected system that can reveal the impedance matching relationship between multiple inverters, local loads, and the grid is established under weak grid conditions. Furthermore, a mapping model is proposed to reflect the excitation effect of the harmonic current of the inverter and the background harmonic voltage of the grid side on the resonance of the grid-connected current. The influence of the grid impedance, PV output power, and number of inverters on the resonance characteristics of the grid-connected system is revealed. Finally, the accuracy of the proposed equivalent model is verified through a simulation, and the influence of relevant factors on the PV grid-connected system is analyzed.
In 2020, the building operation energy consumption was expected to reach 1.06 billion tce, accounting for 21.7% of the country’s total energy consumption. Fully utilizing the demand-side energy-saving potential of residential buildings is crucial for realizing sustainable energy development and building a green, low-carbon society. Accordingly, this study proposes a timescale home energy management strategy that considers multiple comfort levels. First, typical household intelligent electrical equipment are classified based on load characteristics, and a corresponding mathematical model is established. Considering the multiscale coupling effect of building indoor environments, multidimensional comfort indices for thermal, acoustic, and optical users are constructed. Based on the transmission characteristics and inertia differences of the electric and thermal energy systems, a different timescale optimal scheduling model is established to minimize the total electricity cost. Specifically, the long-term scale model optimizes the TCA and energy storage battery operation decisions under thermal comfort. Meanwhile, in the short time scale model, the operation decision of the non-temperature-controlled load is optimized considering the user’s auditory and visual comfort, and the corresponding solving algorithm is proposed for the objective model. Finally, simulations are conducted for four typical day scenarios to verify the effectiveness of the proposed strategy.
