IET Energy Systems Integration最新文献

The application of Markowitz and tangency portfolio and Black–Litterman models is extended by the authors to energy portfolio selection in transmission-distribution environments with high penetration of renewable energy. As Transmission System Operator (TSO) and Distribution System Operator (DSO) contextually take mutualistic or conflicting positions in their portfolio selection process, their risk-return interactions and behaviours depend on their subjective views on generation and operation. Here, the financial portfolio allocation tool Black–Litterman Model is adapted to incorporate subjective views of the operators to arrive at more intuitive portfolios. The best portfolios are searched within the acceptable risk-return search space of each operator defined by their Markowitz efficient frontiers (EF), for Pareto-optimising their profits. The tangency portfolio approach, which is generally used to determine the portfolio of risky and risk-free assets in finance, is used here to determine the portfolio of renewable (energy-risky) and fuel-based sources (energy-risk-free). The proposed methodology is adopted in an HV–MV interconnected test system operated by one TSO and two DSOs, having wind, solar, coal, gas and nuclear generation technologies. It is observed that completely customisable portfolios can be constructed for TSO and DSO based on their inherent financial and energy risk-return behaviours and posterior views.

To achieve carbon neutrality, renewable energy-based power systems and hydrogen are increasingly being promoted. A novel electricity-thermal-hydrogen integrated energy system that combines new energy generation, multi-source load, and multiple energy storage is proposed by the authors. To address uncertainties in new energy output, and issues of untimely unit regulation response and large planning tracking errors, a multi-scale scheduling method based on model predictive control (MPC) was proposed. In the day-ahead dispatching stage, an optimal economic dispatching model was established with the lowest system operation cost as the optimisation objective. The model considers equipment investment, operation, maintenance, and peak-to-valley differences in electricity prices. In the intraday dispatching stage, an MPC-based intraday rolling optimisation correction strategy was proposed to cope with contact line power fluctuations caused by prediction errors of new energy and multi-source load. This strategy combines time-domain rolling and feedback correction of the real-time system state to eliminate the influence of uncertainty factors in the microgrid. The MPC-based intraday rolling optimal scheduling model was established in the form of a discrete state space and transformed into a quadratic planning problem to improve the efficiency and accuracy of the model solution. Finally, a typical microgrid was used as an example to verify the effectiveness of the proposed method. Results show that the contact line tracking error can be within 0.025 kW, and the single scheduling time was within 0.14 s.

The widespread use of biogas and biomass fuels in facility agro-industrial parks has led to a significant increase in their carbon emissions. A day-ahead optimal scheduling model for an integrated energy system (IES) is proposed, that considers the coupling of biomass and power to gas (P2G) to reduce carbon emissions during the operation of an industrial park. The proposed model incorporates the two evaluation indices of the economy of and carbon emissions by the IES of the park, and formulates and solves a multi-objective optimization problem by using the ε-constraint method. From among the solutions to the Pareto front, we choose the scheduling strategy that delivers the optimal performance in case of multiple objectives by using the fuzzy decision method. Finally, the validity of the proposed model was verified by considering the IES of an agriculture–industrial park in the northwest region of China.

A Lypunov-based Adaptive Backstepping Control (ABSC) approach is designed for a power Buck converter. This strategy is an advanced version of the Backstepping method utilising Lyapunov stability function to reach a higher stability and a better disturbance rejection behaviour in the practical applications. In addition, to reduce the computational burden and increase ease of implantation, Black-box technique is considered assuming no accurate mathematical model for the system. Nonetheless, in real-time environments, disturbances with wider ranges including: supply voltage variation, parametric variation, and noise can negatively impact the operation of this method. To compensate for this problem, the gains of the controller should be tuned again for better adaptability with the working condition. Therefore, to satisfy this need and enhance the controller's performance, a metaheuristic algorithm is applied in the control scheme called Grey Wolf Optimisation (GWO) algorithm. GWO is a well-behaved nature-inspired algorithm with faster decision-making dynamics along with more accuracy over different optimisation algorithms. To better elaborate the merits of this approach, conventional BSM and PSO-based PID schemes are also designed and tested in different situations.

In autonomous alternating current microgrids, the grid-forming virtual synchronous generators can cooperate with the conventional synchronous generators to improve system inertia and frequency regulation capability. However, undesired active power oscillations between the synchronous generators and grid-forming virtual synchronous generators may trigger their overcurrent protection and even result in a blackout. To explicitly reveal the oscillatory modes over all frequency bands, a high-fidelity full-order state-space model is first developed. A potentially destabilising sub-synchronous oscillation mode resulting from the interaction between grid-forming virtual synchronous generators voltage controller and synchronous generators q-axis damper winding is identified. Other modes reflecting the low-frequency oscillation and frequency restoration dynamics are also assessed. Subsequently, to make a reasonable trade-off between the accuracy and simplicity of system modelling, an enhanced quasi-stationary model dedicated to low-frequency oscillation evaluation is simplified from the full-order type. The enhanced quasi-stationary model features simplicity and low-order benefits, which makes it more practical for multi-generator system analysis. Moreover, by considering the dynamics of synchronous generators field winding and excitation system, the enhanced quasi-stationary model significantly improves the low-frequency oscillation characterisation accuracy compared with the existing quasi-stationary model. The two developed models are comprehensively compared with the existing small-signal models. Real-time simulations based on RT-LAB are conducted to verify the correctness of the theoretical analysis and the accuracy of the proposed small-signal models.

Although there are many methods to improve the fault ride-through (FRT) capability of doubly-fed induction generator (DFIG) systems at present, each method has its shortcomings, especially the applicability under different voltage dips (VDs), so an improved system structure with a dynamic switching topology and a corresponding control scheme is proposed. Based on the mechanism analysis that the series impedance of the stator can effectively reduce the overcurrent on the rotor side, and considering the feasibility of the FRT scheme in engineering, the dynamic switching topology is designed. The selection of theoretical parameters in different cases is also analysed and designed. Simultaneously, to cooperate with the hardware measures, the control scheme of the rotor side converter (RSC) under different conditions is also improved. The RSC can use the control scheme of active flux attenuation to effectively and quickly reduce the overcurrent on the rotor side, and use reactive power support to accelerate the voltage recovery. The novelty of the FRT scheme is that the scheme can dynamically adjust the topology structure and control scheme under different voltage dips. Thus, its ride-through performance during fault is better under different conditions. A simulation model of the improved system structure and control scheme is built on the MATLAB/Simulink platform. Through the comparison of simulation data, the validity and correctness of the proposed FRT scheme are verified.

The rapid increase of photovoltaic (PV) penetration in active distribution networks (ADN) is posing great challenges to traditional voltage control schemes. A two-stage voltage control strategy of ADN is proposed by the authors based on the alternating direction method of multipliers (ADMM) with refined power to hydrogen (P2H) model, considering multiple types of PV forms such as residential photovoltaic cluster (RPVC) and small-scale PV power stations. In the day-ahead stage, the optimal power flow is performed to determine the optimal scheduling results of on-load tap changers and capacitor banks etc. In the intra-day stage, the real-time voltage control strategy is implemented at the distribution network layer to regulate the power of each type of PV, energy storage systems and P2H to further reduce the voltage deviation. At the customer layer, the residential photovoltaic (RPV) within the RPVC is precisely controlled based on the ADMM algorithm to achieve the minimum voltage deviation at each RPV access point. The proposed strategy is tested on the modified IEEE 33-bus and IEEE 69-bus distribution systems, and the simulation results verify its effectiveness in mitigating voltage violations.

A method for optimal energy and power management of microgrids consisting of mega buildings, plug-in electric vehicles (PEVs) and renewable energy sources (RES) with low computation requirements is proposed by the authors. Thermal and electrical loads are considered for the operation scheduling of the microgrid. In case of non-interconnected operation of the microgrid with the main power grid, the proposed method allows the microgrid to meet the power demand by the buildings and distribution loads exploiting only the hosted PEVs, the integrated RES and, if it is necessary or financially optimal, building auxiliary diesel generators. The primary goal of the suggested algorithm is to significantly reduce the overall daily cost of the microgrid's operation while simultaneously meeting a wide range of constraints. The implementation of the method is based on the exploitation of a two-level hierarchical multi-agent system (MAS) at the level of the microgrid. Suitably defined flexibilities of the microgrid's components to change their power are used to implement optimal power dispatch to them. Detailed simulation results indicated that a remarkable cost reduction of 27% can be achieved.

Electricity theft is a great trouble for power companies. As the means of tampering with smart meters continue to increase, the electricity theft behaviours become more diversified and covert, which are difficult to be identified using the existing electricity theft detection method. In addition, the existing methods usually cannot estimate the economic losses caused by electricity theft. To address these issues, a combined unsupervised learning approach for electricity theft detection and loss estimation is proposed in this study. First, three anomaly measurement indexes including the mean index, fluctuation index, and trend index are proposed to capture different anomalies respectively. Then, based on historical electricity consumption data, we develop two unsupervised learning techniques including the sample-to-subsamples decomposition algorithm and clustering algorithm to obtain the typical ranges of index values, and the load samples whose index values are not in the typical ranges will be considered fraudulent. Furthermore, three anomaly measurement indexes are combined to judge whether the load sample is fraudulent, and the user whose most load samples are judged fraudulent will be considered as an electricity thief. Finally, an economic loss estimation method is proposed, which quantifies the losses of electricity theft. Numerical experiments are carried out based on the Irish smart meter dataset, and the results demonstrate the effectiveness and the superior performance of the proposed method compared with a series of electricity theft detection methods.

Battery energy storage system (BESS) is of great significance to ensure underground engineering (UE) microgrid to have reliable power supply. Distributed energy management is one of the solutions that can enhance the microgrid reliability by efficiently scheduling the distributed appliances (such as diesel generator, BESS) to accommodate various scenarios. A distributed energy management model is proposed, which can help in reliable power supply by prolonging the lifetime of BESS and reducing the load loss. Considering the different energy consumption needs and dispatching capabilities of three regions of the UE microgrid, the proposed energy management model distributed dispatches the three regions in UE microgrid. In addition, considering the relationship between the depth of discharge and lifetime, the proposed energy management model also contains the BESS lifetime extension model constructed with dynamic charge-discharge rate and dynamic bidirectional AC/DC converter efficiency. Based on the deterministic optimisation method, the optimal solution of the proposed energy management model is obtained. The effectiveness of the proposed energy management model is validated under six scenarios (that is grid connected mode, off grid mode, partial interconnection mode, interconnection mode, and independent modes with and without power exchange). The simulation results demonstrate that compared with the conventional model, the proposed model can reduce the operation cost by 6.46% and the load loss rate by 0.747%, which helps to improve the reliability of UE microgrid.