Economic Emission Dispatch Optimization in Power Systems

The economic emission dispatch (EED) problem in power systems seeks to determine the optimal generation schedule that simultaneously minimises fuel costs and pollutant emissions while satisfying demand and operational constraints. Traditional economic dispatch focuses solely on cost minimisation, whereas modern approaches incorporate environmental objectives to address climate change and regulatory pressures. The problem is typically formulated as a non-linear constrained optimisation, accounting for generator limits, ramp-rate constraints, transmission losses and valve-point effects. Multi-objective strategies seek Pareto-optimal fronts that reveal trade-offs between cost and emissions, whereas single-objective methods often employ penalty or weighting schemes to combine objectives. Advances in computational power and the proliferation of renewable resources have introduced new layers of complexity. Sophisticated meta-heuristic and evolutionary algorithms have emerged to navigate non-convex search spaces, delivering rapid convergence and resilient solutions under varying demand profiles. This field is of global significance, underpinning the sustainable operation of large grids, microgrids and hybrid energy hubs. Practical implementations range from real-time dispatch in thermal power plants to coordinated scheduling of wind-thermal systems, with the ultimate aim of reducing carbon footprints while preserving system reliability and economic efficiency. Emerging trends include integration of demand-side response, stochastic modelling of renewables and real-time adaptive control frameworks.

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Economic Emission Dispatch Optimization in Power Systems publication trend

The graph below shows the total number of articles in economic emission dispatch optimization in power systems across all publications each year (not limited to Nature Index journals).

Technical terms

Combined Economic Emission Dispatch (CEED): An optimisation problem that minimises both fuel cost and pollutant emissions of power generation under operational constraints.

Multi-objective optimisation: A mathematical approach to find solutions that balance two or more conflicting objectives, often resulting in a set of Pareto-optimal alternatives.

Pareto optimality: A condition where no objective can be improved without degrading at least one other objective, defining the trade-off frontier.

Meta-heuristic algorithm: A high-level problem-independent strategy designed to explore and exploit search spaces for near-optimal solutions.

Valve-point loading effect: Non-smooth variations in generator fuel cost curves caused by steam admission valves, introducing ripples into the cost function.

Price Penalty Factor: A weighting technique that converts multiple objectives into a single composite objective by assigning relative importance to each criterion.

Demand Response (DR): Mechanisms that adjust consumer electricity usage in response to grid conditions or price signals to enhance system flexibility.

References

1. Developing Chaotic Artificial Ecosystem-Based Optimization Algorithm for Combined Economic Emission Dispatch. IEEE Access (2021).
2. Multi-objective whale optimization approach for cost and emissions scheduling of thermal plants in energy hubs. Energy Reports (2022).
3. Solving the environmental/economic dispatch problem using the hybrid FA-GA multi-objective algorithm. Energy Reports (2022).