Key Takeaways & Executive Findings
- •• • The POA-GWO-CSO hybrid algorithm integrates GWO's alpha/beta/delta leadership hierarchy and CSO's horizontal-vertical crossover into the POA framework, directly addressing the local-optimum entrapment and parameter sensitivity that degrade standalone particle swarm, genetic, and basic POA solvers in source-load-storage dispatch; this matters industrially because dispatch decisions must remain robust across volatile wind-solar profiles without repeated parameter retuning. • • Building envelope thermal inertia is formalized through a resistance-capacitance network (Rwall, Cwall, Rwin, Croom) that converts air-conditioning loads into virtual energy storage, enabling a peak-shaving/valley-filling strategy of pre-heating or pre-cooling before peak periods and power reduction during peaks; this reduces reliance on physical battery storage and lowers building cluster energy costs without violating indoor temperature comfort bands. • • The source-side model couples wind and photovoltaic physical output prediction with explicit forecast-error quantification, while the storage-side model enforces battery full life-cycle constraints; this dual treatment prevents the conservative over-sizing that arises when storage requirements are assessed from only the generation or grid side, and it dynamically matches source-load temporal mismatch. • • Validation on the actual Minning Town dataset in Ningxia confirms that the proposed multi-side coordinated strategy improves active distribution network economic operation and renewable consumption rate compared with alternative dispatch strategies, providing a replicable template for regions experiencing high wind and solar curtailment under the dual-carbon framework.
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Abstract
High-penetration renewable integration in the Ningxia region induces substantial wind and photovoltaic curtailment, exposing the limitations of single-sided dispatch formulations that treat generation, demand, and storage independently. This study develops a coordinated source-load-storage interactive optimal dispatch strategy for active distribution networks, solved via a hybrid Pelican Optimization Algorithm-Grey Wolf Optimizer-Crisscross Optimization (POA-GWO-CSO) framework. A detailed building thermal dynamics model is constructed from a resistance-capacitance network, quantifying the thermal storage of envelopes and enabling air-conditioning loads to function as virtual energy storage under user comfort constraints. Source-side wind and photovoltaic output prediction models incorporate forecast-error quantification; storage-side battery life-cycle constraints dynamically match source-load temporal mismatches. A multi-objective dispatch strategy minimizes system operating cost while maximizing renewable consumption. The hybrid algorithm embeds the alpha-beta-delta leadership mechanism of GWO and the horizontal-vertical crisscross operations of CSO into the POA framework, improving local search precision and convergence efficiency. Validation on the actual Minning Town dataset demonstrates that the proposed strategy reduces operating cost and increases renewable utilization relative to conventional dispatch approaches, confirming the effectiveness of multi-side coordination for high-renewable active distribution networks.
1. Introduction
China's dual-carbon target has driven installed renewable capacity to 1.86 billion kW as of November 2025, yet the stochastic and fluctuating output of distributed wind and photovoltaic resources introduces multidimensional operational risk. Storage stations can buffer renewable intermittency and have been widely deployed for large-scale renewable accommodation and inter-temporal energy balancing. However, prevailing dispatch formulations evaluate storage requirements from either the generation side or the grid side alone. This single-sided perspective yields conservative capacity and power schedules, inflating system operating cost and underutilizing the flexibility that demand-side resources could provide.
Building thermal inertia represents an unexploited flexibility reserve. Envelope materials such as cement mortar and mixed mortar possess heat storage capacity, so short-term variations in air-conditioning power do not cause significant occupant discomfort. Prior work has mined demand response from controllable loads such as water heaters but has neglected the flexibility potential of building envelope thermal inertia, leaving the operational flexibility of cooling and heating equipment untapped. This study constructs a detailed building thermal dynamics model based on differential equations that quantify the relationship among building materials, outdoor temperature, solar irradiance, and indoor temperature. Combined with user comfort constraints that set air-conditioning power ranges and indoor temperature intervals, a peak-shaving/valley-filling pre-cooling and pre-heating strategy is designed. The resulting source-load-storage coordinated model, solved by the POA-GWO-CSO hybrid algorithm, is validated on the actual Minning Town dataset to improve renewable consumption and system economics.
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DAI Wendong, JIN Ping (2026). POA-GWO-CSO-Based Coordinated Source-Load-Storage Interactive Optimal Dispatch Strategy for Active Distribution Networks. Acta Energiae Solaris Sinica. https://doi.org/10.19912/j.0254-0096.tynxb.202608_9696
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Frequently Asked Questions
What specific algorithmic failure modes of standalone POA, PSO, and GA motivated the POA-GWO-CSO hybrid, and how does the hybrid resolve them?
Standalone artificial intelligence algorithms applied to active distribution network source-grid-load-storage dispatch exhibit three documented deficiencies: insufficient search range, low solution precision, and susceptibility to local optima, compounded by high sensitivity of optimization results to parameter selection. The POA-GWO-CSO hybrid addresses these by using POA as the base framework, embedding GWO's alpha/beta/delta wolf leadership mechanism to structure the search hierarchy, and applying CSO's horizontal-vertical crossover operations to enhance local search precision. This combination improves both solution accuracy and computational efficiency for the multi-objective dispatch problem.
How is building thermal inertia converted into a dispatchable virtual energy storage resource without violating occupant comfort?
A detailed building thermal dynamics model is constructed from a resistance-capacitance network comprising wall thermal resistances (Rwall), wall thermal capacitances (Cwall), window thermal resistance (Rwin), and room thermal capacitance (Croom). Differential equations quantify the relationship among building materials, outdoor temperature, solar irradiance, and indoor temperature, revealing the mechanism of envelope heat storage. User comfort requirements define the allowable air-conditioning operating power range and indoor temperature interval. Within these bounds, the strategy implements pre-peak power increase for heat storage and peak-period power reduction for cost reduction, i.e., a peak-shaving/valley-filling pre-cooling and pre-heating strategy that shifts thermal energy in time without compromising comfort.
Why does single-sided storage assessment produce conservative and costly dispatch results, and how does the proposed multi-side model correct this?
Prior studies assessed storage requirements from only the generation side or the grid side, a single perspective that yields conservative computation results and increases system operating cost. The proposed source-load-storage multi-side coordinated operation model breaks this limitation by combining three elements: a source-side wind and photovoltaic output prediction model based on physical generation laws with quantified forecast error to provide accurate source signals; a load-side virtual energy storage formulation that converts controllable air-conditioning loads via envelope thermal storage, reducing dependence on physical storage; and a storage-side model incorporating battery full life-cycle constraints to dynamically match source-load temporal mismatch. This tripartite coordination lowers cost and raises renewable consumption.
What empirical evidence validates the strategy, and what operational improvements were observed on the Minning Town dataset?
Validation used the actual dataset from Minning Town, Ningxia, a region facing high renewable penetration and wind/photovoltaic curtailment. Comparative analysis of different optimal dispatch strategies assessed their effects on active distribution network economic operation and renewable energy consumption rate. The results confirm that the proposed POA-GWO-CSO-based source-load-storage coordinated interactive strategy is effective, improving both system operating economics and renewable accommodation relative to alternative strategies. The study thereby provides a validated dispatch template for high-renewable active distribution networks under the dual-carbon framework.
What are the scalability and deployment bottlenecks when extending this building-thermal-inertia virtual storage approach to heterogeneous building clusters?
The approach requires a detailed thermal dynamics model per building type, parameterized by wall resistance-capacitance values, window thermal resistance, and room capacitance, which vary with construction materials such as cement mortar and mixed mortar. Heterogeneous clusters therefore demand building-specific parameter identification and comfort-band definition. The storage station's dispatch center must issue scheduling commands based on the charge-discharge requirements of controllable building loads at each time interval, implying communication and control infrastructure. The POA-GWO-CSO solver's improved convergence efficiency and reduced parameter sensitivity mitigate the computational burden of scaling to larger clusters, but accurate thermal parameter acquisition remains the principal deployment bottleneck.
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