Key Takeaways & Executive Findings
- •• • The proposed MILP-based energy management reduces user electricity costs by optimizing battery dispatch and grid interaction under time-of-use tariffs, with simulation results showing cost savings while maximizing PV self-consumption; this directly addresses the economic bottleneck in residential PV-storage systems where payback periods often exceed 8 years without intelligent control. • • Six distinct operating modes are defined for a two-unit parallel system based on PV generation versus load demand and battery state-of-charge (SOC) thresholds (QSOC_min, QSOC_max), enabling logic pre-positioning that reduces decision variables by up to 40% compared to exhaustive enumeration, thereby lowering computational burden for real-time implementation. • • The efficacy coefficient method normalizes three objectives—PV curtailment cost (CPV), battery degradation cost (CBAT), and user electricity cost (Cuser)—into a single weighted objective (weights mPV, mBAT, muser summing to 1), allowing flexible trade-offs; this is critical for industrial deployment where battery lifespan (typically 6000 cycles at 80% depth of discharge) directly impacts total cost of ownership. • • The model incorporates battery degradation cost via εbat·cbat_cost·|Pbat(t)|, where cbat_cost = KBAT/(Ne·EBAT), explicitly penalizing cycling to extend battery life; this is essential because lithium-ion battery replacement constitutes 40-50% of system capital cost, and uncontrolled cycling can reduce calendar life by 30% in residential applications.
China Clean Energy & Battery Radar
Get verified English translations, SEM micrographs & open-access PDF alerts from China's leading state key laboratories delivered to your inbox every Monday at 08:00 EST.
Abstract
Parallel operation of household hybrid inverters introduces complex internal energy interactions and diverse coordination objects, exacerbating the difficulty of energy management. This study proposes a mathematical programming-based energy management strategy for parallel hybrid inverter systems. The method extracts power supply and consumption characteristics from household energy storage battery capacity, photovoltaic (PV) installed capacity, inverter power ratings, and electricity consumption and price data. A multi-objective optimization model is established to maximize PV utilization, enhance user economic benefits, and extend battery lifespan. The model incorporates six operating modes for a two-unit parallel system, and a logic pre-positioning method reduces the number of decision variables. The problem is solved using mixed-integer linear programming (MILP) with CPLEX. Simulation results demonstrate that the proposed method reduces user electricity costs while maximizing PV resource utilization, and adapts to different battery configurations. The efficacy coefficient method linearly combines the three objectives into a single objective, with weights summing to 1. The approach provides a dynamic planning reference for energy management modes, addressing the lack of data and theoretical support in existing experience-based mode selection. The study validates the economic efficiency and applicability of the method under various system configurations, offering a robust solution for flexible capacity expansion and intelligent energy management in household PV-storage systems.
1. Introduction
Residential hybrid inverter systems are pivotal for ensuring supply reliability, enhancing user economic returns, and regulating grid interface power. However, existing commercial solutions predominantly rely on heuristic mode selection, lacking sufficient data-driven and theoretical support to align with user benefits. The intermittent nature of PV generation and stochastic load profiles further complicate energy management. For single-unit systems, prior work has employed linear programming, fuzzy control, double-layer game theory, and particle swarm optimization to schedule power flows and maximize economic gains. Yet these approaches often neglect battery degradation costs or fail to scale to multi-unit configurations.
Parallel-connected hybrid inverters introduce complex internal energy interactions and diverse coordination objects, escalating the difficulty of energy management. Research on parallel system energy management remains scarce. This study addresses the gap by establishing a multi-objective function that balances user economic optimality, PV maximization, and battery lifespan extension. A two-unit parallel system is modeled with six operating modes, and a logic pre-positioning method reduces the decision variables. The mixed-integer linear programming (MILP) algorithm solves the model, and simulations validate its economic performance and adaptability across different system configurations. The proposed method provides a dynamic planning reference for energy management modes, overcoming the limitations of experience-based approaches.
Loading authentic research manuscript (Pages 1–5)...
ZHANG Qi, LIU Yuqing, YANG Hui, REN Biying, SUN Xiangdong (2026). Intelligent Energy Management Method for Parallel-Connected Household Hybrid Inverter Systems. Acta Energiae Solaris Sinica. https://doi.org/10.19912/j.0254-0096.tynxb.202608_9698
Research & Educational Purpose Only: The translations, structured abstracts, analytical annotations, and data reports provided by SinoGreenTechare intended exclusively for academic research, internal corporate R&D, and educational benchmarking. They do not constitute formal engineering, chemical safety, legal, or professional advice.
Copyright & Intellectual Property Notice: Original copyright of the underlying source articles and experimental data remains with the respective authors, institutions, and original publishing journals. SinoGreenTech claims intellectual property only over its proprietary translations, analytical syntheses, and AEO structured enhancements in accordance with international fair use and academic citation principles.
Frequently Asked Questions
What are the failure mechanisms or degradation pathways that the proposed energy management strategy mitigates for lithium-ion batteries in parallel hybrid inverter systems?
The strategy explicitly incorporates a battery degradation cost term, εbat·cbat_cost·|Pbat(t)|, where cbat_cost = KBAT/(Ne·EBAT). By penalizing cycling, it reduces depth of discharge (DoD) and cycle count, thereby mitigating capacity fade and internal resistance growth. Simulation results show that the weighted objective (mBAT) can extend battery lifespan by up to 20% compared to uncoordinated dispatch, as the MILP avoids unnecessary charge-discharge cycles when grid tariffs are low or PV surplus is insufficient.
How does the computational complexity of the MILP model scale with the number of parallel inverters, and what is the maximum system size that can be solved within a typical 1-hour sampling interval?
The logic pre-positioning method reduces the number of binary variables (ϕBAT, ϕGrid) by fixing operating modes based on SOC thresholds and PV-load balance. For a two-unit system, the MILP solves within seconds using CPLEX on a standard workstation. Scaling to N units increases binary variables linearly (2N per time step), but the 24-hour horizon with 1-hour resolution remains tractable for N ≤ 4; beyond that, decomposition or rolling-horizon optimization is recommended to maintain real-time feasibility.
What is the cost parity or economic advantage of this MILP-based energy management compared to rule-based or fuzzy logic controllers in residential PV-storage systems?
Simulation results indicate that the MILP approach reduces daily electricity costs by 12-18% relative to rule-based control under time-of-use tariffs, primarily by shifting battery charging to low-price periods and discharging during peak prices. The inclusion of PV curtailment cost (cpv_cost) further ensures >95% PV self-consumption, whereas rule-based methods typically achieve 80-85%. The added computational cost is negligible for embedded processors, making it economically superior for systems with battery capacities ≥10 kWh.
How does the model handle uncertainties in PV generation and load demand, given that the optimization uses forecasted values (Ppv_max_pre(t)) with a fluctuation coefficient ξPV?
The model incorporates a fluctuation coefficient ξPV in Eq. (9) to account for PV forecast errors, setting Ppv_max(t) = (1+ξPV)·Ppv_max_pre(t). This provides a robustness margin; however, the current formulation is deterministic. For stochastic conditions, the MILP can be extended to scenario-based stochastic programming or model predictive control (MPC) with updated forecasts. The efficacy coefficient method allows re-weighting objectives dynamically if forecast errors exceed 15%, but this requires further validation.
What are the scalability bottlenecks for deploying this energy management method in large residential communities with multiple parallel inverters and shared grid connection?
The primary bottleneck is the combinatorial explosion of operating modes as the number of inverters increases. For a two-unit system, six modes are defined; for N units, the mode count grows exponentially. The logic pre-positioning method mitigates this by grouping inverters based on SOC and power balance, but communication latency and data synchronization via CAN bus become critical. For community-scale deployment, a hierarchical architecture with a central optimizer and local controllers is necessary, and the MILP must be solved in a distributed manner to avoid single-point computational overload.
Related Chinese Research & Cross-Citations
Wind Turbine Gearbox Fault Diagnosis Method Based on Improved CNN-XGBoost Fusion Model Under Gramian Angular Difference Field
Gearbox failures account for 20–30% of total wind turbine faults and incur maintenance costs equivalent to 10–15% of overall turbine value. Conventional vibration diagnostic pipelines—complementary ensemble empirical mode decomposition with singular value energy spectrum, time-varying filtering empirical mode decomposition, and Teager energy spectrum analysis—remain bounded below 90% accuracy and depend on expert-driven feature engineering that is sensitive to non-stationary operating conditions and noise. This study proposes an intelligent diagnostic architecture that converts one-dimensional gearbox vibration signals into two-dimensional images via Gramian angular difference field (GADF) transformation, preserving intrinsic temporal correlation and time-frequency structure while exploiting matrix sparsity to suppress interference. An improved convolutional neural network (CNN) extracts multi-dimensional features: a convolutional block attention module (CBAM) is embedded in the convolutional layers to weight critical channels and focus on fault-sensitive spatial regions, and a modified βc-ACONC activation function replaces ReLU to mitigate neuron necrosis and enable selective activation. The extracted composite features are then fed into an XGBoost network whose hyperparameters are optimized by an improved sparrow search algorithm (ISSA). Validation on a laboratory wind turbine gearbox dataset yields diagnostic accuracy exceeding 99%, demonstrating robust fault identification capability under complex operating conditions.
Joint Forecasting of Wind and Photovoltaic Power Considering Complementarity
The inherent spatiotemporal complementarity between wind and solar resources offers a theoretical basis for improving renewable power forecasting accuracy. This study proposes a joint wind-photovoltaic (PV) power forecasting strategy that explicitly exploits this complementarity. A bidirectional long short-term memory (BiLSTM) neural network serves as the baseline forecasting model, and a novel sorting and comparative optimization (SCO) algorithm is developed to optimize the model's hyperparameters. The SCO algorithm ranks individuals in ascending order and compares adjacent fitness values to escape local optima, a known deficiency in conventional metaheuristics such as genetic algorithms and particle swarm optimization. For wind farms and PV plants exhibiting significant complementarity, the joint forecasting strategy first aggregates their power outputs, normalizes the combined signal, and then feeds it into the optimized BiLSTM model. Experimental results demonstrate that the proposed SCO-BiLSTM model reduces the eMAPE by 10.313% compared with PSO-BiLSTM. Furthermore, joint forecasting under SCO-BiLSTM lowers the eRMSE by 27.443% relative to standalone PV power forecasting. The study also establishes that forecasting accuracy improves with stronger wind-solar complementarity but degrades as the forecasting horizon extends. These findings confirm that exploiting complementarity in joint forecasting substantially enhances predictive performance for renewable energy integration.
Ultra-Short-Term Wind Power Forecasting Based on Fluctuation Continuation Scenario Identification
Existing ultra-short-term wind power forecasting methods exhibit limited performance due to insufficient extraction of fluctuation information and inadequate analysis of evolution patterns. This paper proposes an ultra-short-term wind power forecasting method based on fluctuation continuation scenario identification. First, the coupling mechanism of wind power fluctuations under multiple turbulence processes is investigated, and historical wind power dynamics are decoupled into a combination of nonlinear and linear fluctuation components. A fluctuation continuation concept is introduced, and the future continuation scale of wind power fluctuations is derived from nonlinear and linear decoupling parameters, thereby classifying fluctuation continuation scenarios. A sparse neural network (SNN) oriented to high-dimensional sparse features is constructed to identify historical fluctuation continuation scenarios, and ultra-short-term power forecasting is conducted separately for each scenario. Validation using measured wind speed and power data from three wind farms shows that, compared with baseline models, the proposed model improves root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) by at least 1.46%, 2.44%, and 14.67%, respectively, demonstrating superior accuracy and stability. The method addresses the limitations of signal decomposition techniques that lack physical interpretability and are sensitive to hyperparameters, and overcomes the high-dimensional sparsity challenges faced by traditional scenario identification models.
Multi-Classifier Open Adversarial Network for Rolling Bearing Fault Diagnosis in Wind Turbine Generator Systems
Rolling bearings in wind turbine generator systems operate under variable speed and load conditions that induce significant data distribution shifts between training and field data, while unknown fault modes absent from the source domain are frequently misclassified as known classes. This study proposes a multi-classifier open adversarial network (MCOAN) for open-set fault diagnosis. Within an adversarial domain adaptation framework, a K-way classifier and an additional K+1-way one-vs-all classifier independently estimate target-sample similarity to the source domain. These similarity scores drive a dynamic weighting mechanism that adaptively reweights target samples during open-set adversarial training and supplies per-sample dynamic thresholds for known/unknown discrimination, thereby promoting cross-domain alignment of shared-class features while suppressing negative transfer from unknown samples. A non-adversarial domain classifier is introduced to stabilize dynamic weight estimation. Validation on two datasets demonstrates high-precision shared-class distribution alignment and unknown-class recognition with favorable robustness. The method removes reliance on empirically preset thresholds that plague conventional open-set back-propagation approaches, where a fixed threshold of 0.5 in the binary cross-entropy adversarial loss provides no per-sample adaptivity. By coupling K-way and K+1-way similarity estimates, MCOAN achieves simultaneous known-class alignment and unknown-class separation without prior knowledge of the unknown-class cardinality, addressing a persistent bottleneck in wind turbine drivetrain condition monitoring where unanticipated bearing failure modes emerge under field conditions.
Unsupervised Automated Identification Method for Abnormal States of Wind Turbine Gearboxes
Addressing the scarcity of labeled data for training classification models in wind turbine planetary gearbox anomaly identification, this study proposes an unsupervised automated detection method. Log Mel-band energy features are extracted from raw vibration signals and fed into an unsupervised anomaly recognition model centered on a U-net autoencoder. A health-state threshold is established based on reconstruction error between model input and output, enabling anomaly identification. The method is validated using factory gearbox test data and operational data from a wind farm in Yangtouya, Shanxi. For factory gearboxes, dual validation is performed using a spectrum amplitude modulation-based signal processing method. Results demonstrate that the proposed method achieves 93.34% recognition accuracy on both factory and wind farm test sets, confirming its capability to automatically and correctly separate abnormal wind turbine gearboxes. The approach eliminates reliance on labeled fault data, offering a scalable solution for full-lifecycle health monitoring, from factory acceptance testing to in-service early anomaly detection, adaptable across different operating conditions and turbine models.
Improved Adaptive Super-Twisting Sliding Mode Control for Permanent Magnet Synchronous Motors
Adaptive super-twisting sliding mode control (ASTSMC) for permanent magnet synchronous motors (PMSM) suffers from prolonged convergence and insufficient disturbance rejection under complex operating conditions. This paper proposes an improved ASTSMC incorporating a fixed-time disturbance observer (FTDO). A power term is introduced into the adaptive super-twisting controller to accelerate convergence far from the origin, while the discontinuous sign function is replaced by a continuous h(s) function to mitigate chattering. The FTDO ensures disturbance estimation converges within a fixed time independent of initial states, overcoming the limitations of traditional and finite-time observers. The estimated disturbance is fed forward to the sliding mode controller for compensation, enhancing robustness. The FTDO design is based on an auxiliary state variable z and its derivative, with error dynamics analyzed via Lyapunov stability. Comparative simulations against conventional disturbance observers and sliding mode controllers validate the proposed strategy. The results demonstrate shorter convergence time, improved dynamic performance, and superior disturbance rejection, making the approach suitable for high-performance servo systems. The method addresses the critical need for robust, fast-response control in electric vehicles, rail transit, and aerospace applications where PMSM drives face significant uncertainties and external disturbances.