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This study proposes a cuckoo search algorithm (CSA) for solving non-convex economic dispatch (ED) considering generator and system characteristics including valve-point effects, multiple fuels, prohibited zones, spinning reserve and power loss. CSA is a new meta-heuristic optimisation method inspired from the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds of other species. When the host birds discover an alien egg in their nest, they can either throw it away or simply abandon their nest and build a new one elsewhere. The CSA idealised such breeding behaviour in combination with Lévy flights behaviour of some birds and fruit flies for applying to various constrained optimisation problems. The effectiveness of the proposed method has been tested on different non-convex ED problems. Test results have indicated that the proposed method can obtain less expensive solutions than many other methods reported in the literature. Accordingly, the proposed CSA is a promising method for solving the practical nonconvex ED problems.

This study proposes a stochastic optimisation model for the day-ahead scheduling in power systems, which incorporates the hourly demand response (DR) for managing the variability of renewable energy sources (RES). DR considers physical and operating constraints of the hourly demand for economic and reliability responses. The proposed stochastic day-ahead scheduling algorithm considers random outages of system components and forecast errors for hourly loads and RES. The Monte Carlo simulation is applied to create stochastic security-constrained unit commitment (SCUC) scenarios for the day-ahead scheduling. A general-purpose mixed-integer linear problem software is employed to solve the stochastic SCUC problem. The numerical results demonstrate the benefits of applying DR to the proposed day-ahead scheduling with variable RES.

A carbon credit is a permit to allocate specific carbon emissions and can also be converted into a traded commodity through the carbon trading market (CTM). For government agencies and private enterprises, these carbon credits can be traded on the CTM. Under this trading mechanism, the institutions with low‐carbon emissions can obtain the specific benefits and rewards from fossil fuel consumption reduction, carbon emission reduction, and remaining carbon emissions credits, thereby incentivizing them to reduce greenhouse gas (GHG) emissions and achieve the overall carbon reduction goals. Carbon emissions can be reduced from either power generation ends or high‐level pollution electricity‐consumer ends. Under the schemes of regulated and voluntary markets, the target of carbon emission reduction can be achieved within a foreseeable timeframe by using the power generation proportions adjustment, saving energy on the electricity‐consumer ends, strengthening energy management and efficiency, and increasing renewable energy penetration in the electrical grid. However, power generation significantly has major contributions to carbon dioxide (CO2) and other GHG emissions. For economic dispatch (ED) and carbon emission allocation, this study investigates the ED benefits of power generation proportions adjustment with increasing the generation capacity of natural gas and renewable energy in perspective of power generation end. Four typical scenarios are designed and advanced strategy with different power generation proportions to obtain the fuel cost of various generation units, the carbon emissions of various fuel units, and the benefits of carbon emission reductions, which are used to validate the benefits of increasing the proportions of natural gas generations and renewable energy as decreasing the proportions of oil fuel generations. Considering various scenarios, fuel costs, pollution emissions, and increasing renewable energy, the power generation, carbon emissions, and allowable carbon emissions of various generating units can be estimated by using the swarm intelligence optimization algorithm, such as the particle swarm optimization algorithm (PSOA), which can offer the carbon credit allocation and trading information in the CTM. This study investigates the economic dispatch (ED) benefits of increasing the generation capacity of natural gas and renewable energy and carbon emission allocation in power generation, with changes in the current power generation proportions. Four scenarios with different power generation proportions were designed to obtain the fuel cost of power generation, the carbon emissions of various fuel units, and the benefits of carbon emission reductions, which are used to validate the benefits of increasing the capacity of natural gas generation units and renewable energy and decreasing the capacity of oil fuel generation units.

In this paper, a consensus based fully distributed optimization algorithm is proposed for solving economic dispatch problem (EDP) in smart grid. Since the incremental cost of all buses reach consensus when the optimal solution is achieved, it is selected as a consensus variable. An additional variable at each bus, called “surplus” is added to record the local power mismatch, which is used as a feedback variable to purse the balance between power supply and demand. Different from most of the existing distributed methods which require the communication network to be balanced, the algorithm uses a row random matrix and a column random matrix to precisely steer all the agents to asymptotically converge to a global optimal solution over a time‐varying directed communication network. Due to the use of a fixed step size, the proposed algorithm also outperforms other algorithms in terms of convergence speed. The graph and eigenvalue perturbation theories are employed for the algorithm convergence analysis, and the upper bound of the parameters required for convergence is given theoretically. Finally, the performance and scalability of the proposed distributed algorithm are verified by several case studies conducted on the IEEE 14‐bus power system and a 200‐node test system. A consensus based fully distributed optimization algorithm is proposed for solving EDP in smart grid. Our algorithm uses a row random matrix and a column random matrix to precisely steer all the agents to asymptotically converge to a global optimal solution over a time‐varying directed communication network. Experimental results show that the proposed distributed algorithm has good performance and scalability.

The rapid development of renewable energy has highlighted the issue of its accommodation, which has become a critical challenge for power grids with high renewable energy penetration. Accurately assessing a grid’s renewable energy accommodation capability is essential for ensuring power grid operational security, as well as for the rational planning and efficient operation of renewable energy sources and adjustable power resources. This paper adopts a long-term chronological power balance simulation approach, integrating the dynamic balance among multiple types of power sources, loads, and outbound transmission. Dispatch schemes suitable for different types of power sources, including hydropower, thermal power, wind power, solar power, and nuclear power, were designed based on their operational characteristics. Key operational constraints, such as output limits, staged water levels, pumping/generation modes of pumped storage, and nuclear power regulation duration, were considered. A refined analysis model for renewable energy accommodation in regional power grids was constructed, aiming to maximize the total accommodated renewable energy electricity. Using actual data from the Northeast China Power Grid in 2024, the model was validated, showing results largely consistent with actual accommodation conditions. Analysis based on next-year forecast data indicated that the renewable energy utilization rate is expected to decline to 90.6%, with the proportion of curtailment due to insufficient peaking capacity and grid constraints expanding to 8:2. Sensitivity analysis revealed a clear correlation between the renewable energy utilization rate and the scale of newly installed renewable capacity and energy storage. It is recommended to control the expansion of new renewable energy installations while increasing the construction of flexible power sources such as pumped storage and other energy storage technologies.

In view of the problem of weak source‐load matching and insufficient optimisation capability of multi‐objective solution methods in microgrids, an improved reference point non‐dominated sorting genetic algorithm (NSGA‐III) is proposed for the optimal configuration of interconnected multi‐source microgrids. Firstly, considering the demand response (DR) of user load, the load curve is optimised and the real‐time electricity price curve is obtained. Based on the information obtained from DR, an optimisation and scheduling model for a wind‐solar‐microturbine‐storage‐diesel microgrid is established with the objectives of minimising economic cost and the minimum emission of pollutants. The improved entropy formula is used to determine the switching timing of evolution strategies, and the NSGA‐III algorithm improved by differential evolution (DE) is used to solve the microgrid optimisation problem. Furthermore, the technique for order of preference by similarity to ideal solution (TOPSIS) is employed to determine the weight of the two objectives, and a compromise solution is selected from the Pareto front (PF). Finally, the case study is conducted to demonstrate that the introduction of DR strategies can effectively improve the load curve, and the improved NSGA‐III algorithm can further enhance the diversity of the population, validating the rationality and feasibility of the proposed method. An improved reference point NSGA‐III is proposed for the optimal configuration of interconnected multi‐source microgrids. Considering the DR of user load, the load curve is optimised and the real‐time electricity price curve is obtained. An optimisation and scheduling model for a wind‐solar‐microturbine‐storage‐diesel microgrid is established with the objectives of minimising economic cost and the minimum emission of pollutant.

Due to the generation variability, the growing capacity of renewable energy has posed unprecedented challenges to ensure the security of power system operation. Here, a two‐stage strengthened distributionally robust optimization (DRO) scheme is proposed for theself‐scheduling of a combined heat and power virtual power plant (CHP‐VPP) over a coupled electric power network (EPN) and district heating network (DHN). The CHP‐VPP operator maximizes its profits in the day‐ahead market and minimizes its cost in the real‐time market under the worst‐case realization of the uncertainties. Instead of assuming that the uncertainties follow known probability distributions or confidence bounds, a strengthened ambiguity set based on moment information and Wasserstein metric is built to provide more accurate characterizations of the true probability distribution of uncertainties. In addition, in order to enhance the flexibility of the system, a HOMIE model considering indoor activities and outside temperatures of each building is built to satisfy the comfortable indoor temperature. To make the whole problem tractable, linearisation and duality theory are adopted, and then a tailored column‐and‐constraint generation algorithm is developed to solve the problem. The validity and applicability of the strengthened DRO scheme are verified by an IEEE 33‐bus EPN and 14‐node DHN.

Dynamic economic emission dispatch problems are complex optimization tasks in power systems that aim to simultaneously minimize both fuel costs and pollutant emissions while satisfying various system constraints. Traditional methods often involve solving intricate nonlinear load flow equations or employing approximate loss formulas to account for transmission losses. These methods can be computationally expensive and may not accurately represent the actual transmission losses, affecting the overall optimization results. To address these limitations, this study proposes a novel approach that integrates transmission loss prediction into the dynamic economic emission dispatch (DEED) problem. A Random Forest machine learning model was offline-trained to predict transmission losses accurately, eliminating the need for repeated calculations during each iteration of the optimization process. This significantly reduced the computational burden of the algorithm and improved its efficiency. The proposed method utilizes a powerful multi-objective stochastic paint optimizer to solve the highly constrained and complex dynamic economic emission dispatch problem integrated with random forest-based loss prediction. A fuzzy membership-based approach was employed to determine the best compromise Pareto-optimal solution. The proposed algorithm integrated with loss prediction was validated on widely used five and ten-unit power systems with B-loss coefficients. The results obtained using the proposed algorithm were compared with seventeen algorithms available in the literature, demonstrating that the multi-objective stochastic paint optimizer (MOSPO) outperforms most existing algorithms. Notably, for the Institute of Electrical and Electronics Engineers (IEEE) thirty bus system, the proposed algorithm achieves yearly fuel cost savings of USD 37,339.5 and USD 3423.7 compared to the existing group search optimizer algorithm with multiple producers (GSOMP) and multi-objective multi-verse optimization (MOMVO) algorithms.

Developing a low-carbon power system is critical and fundamental to cope with the challenges of global warming, in which the carbon capture and storage (CCS) technology will play a key role. In this study, the characteristics of energy flow and operation of carbon capture plants (CCPs) are clarified, while the mutual constraint between total generation output of CCPs and operation power consumption of carbon capture system is analysed. Then a generation output model and the optimal dispatch principle of CCPs is established, which can identify how the amount of carbon captured can represent a premium payment that can offset the increase in costs caused by the reduction on power output due to the CCS. On this basis, what with the low-carbon economy factors, a economic power dispatch model under low-carbon economy with CCPs considered is proposed. With the generation fuel cost and carbon emission cost incorporated in the objective function, the model proposed can effectively evaluate the power dispatch problem under low-carbon economy. Studies of the economic power dispatch of the 3-unit, 26-unit and 54-unit test systems show that the model proposed is effective and practical.

Economic dispatch of power is no more a sole concern for utilities. Instead, the utilities focus on reducing toxic gases emitted to the atmosphere due to the maximum utilisation of conventional fossil‐fuelled generators to meet the surging demand for electricity. This can be carried out by involving renewable energy sources (RES) to generate clean power compensating the depletion in the availability of fossil fuels. This article performs combined economic emission disfpatch (CEED) on four dynamic systems with and without the involvement of RES. Two methods for solving CEED, namely the price‐penalty factor (ppf) method and the fractional programming (FP) method, are used to perform CEED for all the four test systems, and a comparative analysis between them is made based on the least emission of harmful and toxic gases into the atmosphere. A novel hybrid (CSA‐JAYA) algorithm is used as the optimisation tool for the study. Numerical results manifest that the FP method of solving CEED is economic and emits less toxic gases to the atmosphere than the ppf method. The proposed hybrid CSA‐JAYA outperformed a long list of algorithms from recent literature in consistently providing better and superior quality solutions.