Distributed economic dispatch for microgrids with vehicle-to-grid under time-varying network

Low-carbon economic dispatch of electrified transportation networks considering grid parity and mixed traffic of human-driven and autonomous vehicles

Low carbon economic dispatch of electric-hydrogen-blended natural gas integrated energy system with multi-energy coupling and integrated demand response

ADMM-Based distributed economic dispatch approach for distributed generators and energy storage systems with time-varying network

Hybrid deep reinforcement learning for economic dispatch in port microgrids

A hierarchical resilient economic dispatch strategy for multi-energy system under DoS attacks

Synergistic Low-Carbon Economic Dispatch of a Virtual Power Plant with Microencapsulated Carbon Capture and Demand Response

The optimization of economic dispatch in hybrid diesel photovoltaic systems within Non-Interconnected Zones (NIZ) is essential to enhance energy sustainability and reduce operating costs. The variability of renewable generation and the uncertainty of electricity demand hinder efficient planning, underscoring the need for advanced optimization models. The purpose of this research was to develop an economic dispatch model for diesel generators integrated with photovoltaic generation, incorporating electricity demand forecasting. The methodology was based on formulating a quadratic programming problem and applying vector autoregressive models supported by socioeconomic variables. Simulations were carried out in Python using the IPOPT (Interior Point Optimizer) solver. The proposed model aimed to optimize operational efficiency by reducing CO₂ emissions and production costs. The analysis was applied to a modified version of the IEEE 33-bus distribution system. The results showed that the optimal dispatch reduced generation costs by 32.1%, decreasing from USD 15 853.83 in the base scenario to USD 10 769.82 with the inclusion of photovoltaic generation. Likewise, daily fuel consumption decreased by 4 227.4 gallons, while CO₂ emissions were reduced by 41 926.1 kg. In addition, solar generation contributed 4 249.2 kWh per day, equivalent to 5.09% of total demand, directly reducing technical losses from 292 kW to 243 kW. In conclusion, the results demonstrate that the integration of predictive models and optimization techniques improves operational performance and supports sustainable energy planning in isolated communities.

Addressing uncertainties on the demand side caused by electricity price fluctuations during integrated energy system (IES) dispatch, modeling biases resulting from static assumptions about equipment energy efficiency, and cost redundancy issues stemming from unreasonable seasonal allocation of carbon quotas, this study constructs an electricity PDR economic dispatch optimization model incorporating dynamic energy efficiency and dynamic carbon trading. It proposes a “distributed robust optimization (DRO)-model predictive control (MPC)” collaborative framework and a tiered dynamic carbon quota allocation strategy accounting for seasonal output and efficiency variations of equipment, tailored to match carbon emission characteristics across different seasons. At the demand response level, an electricity price elasticity coefficient matrix is introduced to quantify the impact of real-time price fluctuations on load, integrating it into the MPC model to resolve the time-scale mismatch between day-ahead and intraday scheduling. Simulation results demonstrate: The coupled dynamic energy efficiency and carbon trading model reduces total system costs by 13.07% and carbon trading costs by 11.57% compared to the conventional approach. Regarding tracking error, the combination of rolling optimization and feedback correction improves tracking accuracy by 14.66% and 6.13% compared to cases without feedback correction and rolling optimization, respectively, while reducing total costs by 4.36% compared to the case without rolling optimization. This study provides a scientifically feasible optimization solution for low-carbon economic dispatch of IES under uncertainty.

A large model accelerated BERT distributed multi-objective economic dispatch method for novel power systems with large-scale renewable energy sources

An initialization-free distributed prescribed-time optimization algorithm based on multiagent systems for solving economic dispatch problem

Enhanced Economic Dispatch Optimization Using Momentum in the Orca Predation Algorithm

Fixed-time safe distributed optimization for cost-coupled weighted economic dispatch over time-varying networks

Deep learning-enhanced economic dispatch for integrated microgrid-water systems with desalination

Abstract The study addresses rising global energy demand by optimizing an integrated cooling, heating, and power (CCHP) system. This study introduces a reverse-learning Whale Optimization Algorithm (RL-WOA) to accelerate convergence and improve dispatch optimality within the scheduling model. The CCHP is modeled to simulate multi-energy production, demand, and storage interactions, and evaluated on practical operational data. A carbon trading system (CTS) is embedded to quantify economic and environmental impact, incorporating tiered pricing and explicit treatment of storage-related emissions. RL-WOA achieves the advanced optima in ~200 iterations versus 350 for standard WOA, reducing computational time while enhancing solution quality. The CTS deployment lowers both cost and emissions, a tiered CTS produces the lowest emissions (2.15 t), and excluded storage emissions reduces costs by $14.58 t⁻¹. Results demonstrate that combining RL-WOA with CTS materially improves energy-carbon co-optimization in CCHP scheduling. The framework offers a practical pathway to balance efficiency, sustainability, and economic viability, and motivates future work on combined energy–carbon market dynamics.

A Parallel Hybrid Forecasting Framework for Economic Dispatch: With Applications for China's Electricity Market Operations

Dynamic Economic Dispatch Using Mixed-Integer Linear Programming for Indonesian Electricity System Integrated with Cascaded Hydropower Plant Considering Take or Pay Contract

Power generation and transmission systems face increasing challenges in coordinating maintenance planning under economic pressure and carbon emission constraints. This study proposes an optimization framework that integrates preventive maintenance scheduling with operational dispatch decisions, aiming to achieve both cost efficiency and emission reduction. A bi-layer scenario-based mixed-integer optimization model is formulated, where the upper layer determines annual preventive maintenance windows, and the lower layer performs hourly economic dispatch considering renewable generation and demand uncertainty. To manage the exposure to extreme carbon outcomes, a Conditional Value-at-Risk (CVaR) constraint is embedded, jointly controlling economic and environmental risks. A parallel cut-generation decomposition algorithm is developed to ensure computational scalability for large-scale systems. Numerical experiments on six-bus and IEEE 118-bus systems demonstrate that the proposed model reduces total carbon emissions by up to 32.1%, while maintaining cost efficiency and system reliability. The scenario analyses further show that adjusting maintenance schedules according to seasonal carbon intensity effectively balances operation and emission targets. The results confirm that the proposed optimization framework provides a practical and scalable approach for achieving low-carbon, reliable, and economically efficient power system maintenance planning.

Under the dual targets of carbon peaking and carbon neutrality, virtual power plants (VPPs) are expected to coordinate distributed energy resources in distribution networks to ensure low-carbon operation. This paper introduces a distribution-level dynamic carbon emission factor (DCEF), derived from nodal carbon potentials on an IEEE 33-bus distribution network, and uses it as a time-varying carbon signal to guide VPP scheduling. A bi-objective ε-constraint mixed-integer linear programming model is formulated to minimise daily operating costs and CO2 emissions, with a demand response and battery storage being dispatched under network constraints. Four seasonal typical working days are constructed from measured load data and wind/PV profiles, and three strategies are compared: pure economic dispatch, dispatch with a static average carbon factor, and dispatch with the proposed spatiotemporal DCEF. Our results show that the DCEF-based strategy reduces daily CO2 emissions by up to about 8–9% in the typical summer day compared with economic dispatch, while in spring, autumn, and winter, it achieves smaller but measurable reductions in the order of 0.1–0.3% of daily emissions. Across all seasons, the average and peak carbon potential are noticeably lowered, and renewable energy utilisation is improved, with limited impacts on costs. These findings indicate that feeder-level DCEFs provide a practical extension of existing carbon-aware demand response frameworks for low-carbon VPP dispatch in distribution networks.

ABSTRACT This paper presents a bi‐objective optimisation approach for grid‐connected microgrids, aiming to minimise operational costs and voltage deviation at the connection nodes of distributed energy resources and loads. Existing research typically addresses these objectives separately, and the simultaneous consideration of economic performance and voltage deviation in grid‐connected community microgrids with multiple generation resources remains in an early stage of development. To advance the research in this area, a novel mean‐guided elite selection genetic algorithm (MGES‐GA) is proposed to enhance the balance between convergence and diversity in multi‐objective optimisation. The proposed algorithm enhances the selection process by re‐evaluating low‐performing individuals through gene mixing with elite solutions, thereby preserving diversity and avoiding premature convergence. Comparative analysis of the MGES‐GA with the enhanced genetic algorithm, differential evolution with heuristic, and improved differential evolutionary optimisation algorithms demonstrates its superior performance in optimising the economic dispatch of a grid‐connected microgrid. In a bi‐objective comparison with state‐of‐the‐art algorithms, tested on a modified IEEE European low‐voltage test feeder and IEEE 33‐bus network, MGES‐GA demonstrates its effectiveness in balancing conflicting objectives by producing lower voltage deviations at comparable or lower costs.