Search Results (30)

Under the “dual carbon” goals, the low-carbon economic dispatch of integrated energy systems (IES) faces multiple challenges, including suboptimal economic efficiency, excessive carbon emissions, and limited renewable energy integration. While traditional green certificate trading (GCT) enhances renewable energy adoption, its emission reduction effect remains inadequate. Conversely, standalone carbon emission trading (CET) effectively curbs emissions but often at the expense of increased operational costs, making it difficult to achieve both economic and environmental objectives simultaneously. To address these limitations, this study proposes an innovative green certificate trading–tiered carbon emission trading (GCT–CET) synergistic mechanism integrated with demand-side flexible load optimization, developing a low-carbon dispatch model designed to minimize total system costs. Simulation experiments conducted with the CPLEX solver demonstrate that, compared to individual GCT or CET implementations, the proposed coordinated mechanism effectively combines renewable energy incentives (through GCT) with stringent emission control (via stepped CET), resulting in a 47.8% reduction in carbon emissions and a 5.4% decrease in total costs. Furthermore, the participation of flexible loads enhances supply–demand balancing, presenting a transformative solution for achieving high-efficiency and low-carbon operation in IES. Full article

This study proposes an improved multi-objective black-winged kite algorithm (MOBKA-QL) integrating Q-learning with adaptive mutation strategies for optimizing multi-objective scheduling in integrated energy systems (IES). The algorithm dynamically selects mutation strategies through Q-learning to enhance solution diversity and accelerate convergence. First, an optimal scheduling model is established, incorporating a carbon capture system (CCS), power-to-gas (P2G), solar thermal, wind power, and energy storage to minimize economic costs and carbon emissions while maximizing energy efficiency. Second, the heat-to-power ratio of the cogeneration system is dynamically adjusted according to load demand, enabling flexible control of combined heat and power (CHP) output. The integration of CCS+P2G further reduces carbon emissions and wind curtailment, with the produced methane utilized in boilers and cogeneration systems. Hydrogen fuel cells (HFCs) are employed to mitigate cascading energy losses. Using forecasted load and renewable energy data from a specific region, dispatch experiments demonstrate that the proposed system reduces economic costs and CO₂ emissions by 14.63% and 13.9%, respectively, while improving energy efficiency by 28.84%. Additionally, the adjustable heat-to-power ratio of CHP yields synergistic economic, energy, and environmental benefits. Full article

In integrated energy system modeling, extant research predominantly addresses single-energy system optimization or carbon emission flow models, failing to adequately elucidate the mechanisms of combined energy and carbon flow modeling in complex energy systems. This deficiency hampers a thorough analysis of the coupling relationships between energy and carbon flows, thereby posing significant challenges for resource allocation and carbon mitigation within integrated energy systems. This paper presents an innovative energy–carbon coupling model, constructing a unified framework for energy and carbon flow modeling centered on the energy hub, thereby overcoming the limitations of traditional approaches that are unable to model both energy and carbon flows concurrently. The model comprehensively examines the coupling nodes and carbon density correlations among energy conversion devices within multi-energy systems, precisely quantifying carbon emission paths and distribution across devices. This provides a novel methodology for carbon emission management in integrated energy systems. Case studies on typical integrated energy systems demonstrate the proposed model’s efficacy in low-carbon economic dispatch. The energy–carbon coupling model developed in this study offers a high-adaptability solution for integrated energy systems in multi-energy, low-carbon parks, achieving an optimal balance between economic efficiency and environmental performance under dual objectives of energy demand and carbon emission minimization. Full article

As the global energy structure undergoes transformation and the low-carbon development process continues to advance, integrated energy systems have progressively emerged as crucial technical support for achieving sustainable development. In this paper, a joint-day optimal scheduling model is put forward considering the existence of dispatchable resources in community integrated energy systems (CIES). The aim is to cut down the system operation cost and enhance energy utilization efficiency. This model is founded on the concept of energy hubs and combines the shiftable, transferable, and reducible characteristics of demand-side flexible loads. It includes gas turbine power generation systems, energy storage, as well as wind and solar renewable resources. System operation cost and carbon trading cost are comprehensively taken into account, and ultimately, the CIES low-carbon economic dispatch model with the lowest total cost as the optimization objective is established. The Yalmip toolbox and Cplex solver are employed to solve the model. The optimization results of flexible electric and thermal loads participating in dispatching under different scenarios are analyzed through simulation. The economic benefits of electric and thermal independent dispatching are compared and analyzed, and the economic benefits of electric and thermal coupled dispatching are verified. The study reveals that the rational scheduling of user-side flexible loads can notably reduce operating costs, lower the load peak-to-valley difference and carbon emissions, and boost the comprehensive economic and environmental benefits of the system. Full article

Onshore gas-to-wire is considered for 6.5 MMSm³/d of natural gas, with 44% mol carbon dioxide coming from offshore deep-water oil and gas fields. Base-case GTW-CONV is a conventional natural gas combined cycle, with a single-pressure Rankine cycle and 100% carbon dioxide emissions. The second variant, GTW-CCS, results from GTW-CONV with the addition of post-combustion aqueous monoethanolamine carbon capture, coupled to carbon dioxide dispatch to enhance oil recovery. Despite investment and power penalties, GTW-CCS generates both environmental and economic benefits due to carbon dioxide’s monetization for enhanced oil production. The third variant, GTW-CCS-EGR, adds two intensification layers over GTW-CCS, as follows: exhaust gas recycle and a triple-pressure Rankine cycle. Exhaust gas recycle is a beneficial intensification for carbon capture, bringing a 60% flue gas reduction (reduces column’s diameters) and a more than 100% increase in flue gas carbon dioxide content (increases driving force, reducing column’s height). GTW-CONV, GTW-CCS, and GTW-CCS-EGR were analyzed on techno-economic and environment–thermodynamic grounds. GTW-CCS-EGR’s thermodynamic analysis unveils 807 MW lost work (79.8%) in the combined cycle, followed by the post-combustion capture unit with 113 MW lost work (11.2%). GTW-CCS-EGR achieved a 35.34% thermodynamic efficiency, while GTW-CONV attained a 50.5% thermodynamic efficiency and 56% greater electricity exportation. Although carbon capture and storage imposes a 35.9% energy penalty, GTW-CCS-EGR reached a superior net value of 1816 MMUSD thanks to intensification and carbon dioxide monetization, avoiding 505.8 t/h of carbon emissions (emission factor 0.084 t^CO2/MWh), while GTW-CONV entails 0.642 t^CO2/MWh. Full article

Reducing carbon emissions and increasing the integration of new energy sources are key steps towards achieving sustainable development. Virtual power plants (VPPs) play a significant role in enhancing grid security and promoting the transition to clean, low-carbon energy. The core equipment of the VPP, the CHP unit, utilizes a thermal engine or power station to generate electricity and useful heat simultaneously. However, the intermittent and volatile nature of renewable energy sources, as well as the “heat-driven power generation” mode of combined heat and power (CHP) units, presents contradictions that severely affect their peak-shifting capability and lead to high carbon emissions. To address these issues, a novel VPP is established by integrating traditional power plants with carbon capture and hydrogen energy storage. This approach utilizes a “hydrogen energy storage–electric boiler” decoupling method to address the operational mode of CHP, strengthens the coupling relationship between electric and thermal hydrogen loads, and considers a tiered carbon-trading mechanism. With the net profit of the VPP as the optimization objective, the model balances economic and environmental considerations and establishes a low-carbon economic dispatch model for the VPP. A genetic algorithm is employed for solving, and three different dispatch strategies are set for simulation in three distinct seasonal scenarios. The comprehensive comparative analysis of the dispatch results reveals a reduction in carbon emissions and an increase in net profit to varying degrees across all three seasons. Overall, the proposed dispatch strategy demonstrates the ability to enhance the new energy-integration capacity and total revenue of a VPP while simultaneously achieving the goal of reducing carbon emissions. Full article

This paper presents an exploration of the potential of hybrid renewable energy systems (HRESs), combining floating solar photovoltaics (FPV), wind turbines, and vanadium redox flow (VRF) battery energy storage systems (BESSs) to expedite the transition from conventional to renewable energy for the mining sector in South Africa. The feasibility study assesses how to enhance the overall efficiency and minimize greenhouse gas emissions from an economic standpoint by using the Hybrid Optimization of Multiple Energy Resources (HOMER) grid software version 1.11.1 and PVsyst version 7.4. Furthermore, the BESS Covariance Matrix Adaptation Evolution Strategy (CMA-ES) dispatch algorithm is proposed to make the most of the battery storage capacity and capability, aligning it with the dynamic energy demand and supply patterns of an HRES. The proposed HRES includes a highly efficient SFPV with a performance ratio of 0.855 and an annual energy production of 15,835 MWh; a wind turbine (WT) operating for 2977 h annually, achieving a 25% wind penetration rate; and a dynamic VRF-BESS with a 15,439 kWh life throughput and a 3 s dispatch response time. This HRES has a CapEx of R172 million, a 23.5% Internal Rate of Return (IRR), and an investment payback period of 4.9 years. It offers a low Levelized Cost of Energy (LCoE) at 4.27 R/kWh, a competitive Blended Cost of Energy (BCoE) at 1.91 R/kWh, and a positive net present cost (NPC), making it economically advantageous without external subsidies. Moreover, it annually reduces CO₂ emissions by 1,715,468 kg, SO₂ emissions by 7437 kg, and NOx emissions by 3637 kg, contributing to a significant environmental benefit. Full article

A low-carbon economic optimization dispatch model of integrated energy system is proposed to improve the low-carbon and economic efficiency of the integrated energy systems. Firstly, the waste heat generator with the organic Rankine cycle is introduced into the combined heat and power to decouple the combined heat and power operation, and a coupled model with an organic Rankine cycle, power to gas, combined heat and power and carbon capture system is established. Then, the ladder-type carbon trading mechanism is introduced to improve the low-carbon model. Finally, the function is established to minimize the sum of energy purchase costs, operation and maintenance costs, and environmental costs. The proposed integrated energy systems’ low-carbon economic dispatch model reduces the total operating cost by 18.9% and the carbon emissions by 83.7% by setting up different models for comparative analysis. Full article

Thermal power plants use coal as a fuel to create electricity while wasting a significant amount of energy as heat. If the heat and power plants are combined and used in cogeneration systems, it is possible to reuse the waste heat and hence enhance the overall efficiency of the power plant. In order to minimize production costs while taking system constraints into account, it is important to find out the optimal operating point of power and heat for each unit. Combined heat and power production is now widely used to improve thermal efficiency, lower environmental emissions, and reduce power generation costs. In order to determine the best solutions to the combined heat and power economic dispatch problem, several traditional as well as innovative heuristic optimization approaches were employed. This study offers a thorough analysis of the use of heuristic optimization techniques for the solution of the combined heat and power economic dispatch problem. In this proposed work, the most well-known heuristic optimization methods are examined and used for the solution of various generating unit systems, such as 4, 7, 11, 24, 48, 84, and 96, taking into account various constraints. This study analyzes how various evolutionary approaches are performed for various test systems. The heuristic methodologies’ best outcomes for various case studies with restrictions are contrasted. Full article

Economic dispatch (ED) attempts to find the most cost-effective combination of power generation units while meeting operational constraints. Another problem that can’t be resolved by standard economic dispatch problems is figuring out the method of generating dispatch that would be most cost-effective in meeting the local demand without exceeding the tie-line capacity. Making a trade-off between fuel costs and environmental concerns, a contentious problem in industrialized countries, seems essential. As a result, this study introduces a multi-objective approach for different ED problems, such as multi-area emission economic dispatch (MAEED) and reserve constrained multi-area emission economic dispatch (RCMAEED), when there are real-world restrictions present, like the valve point effect (VPE), prohibited operating zones (POZs), multi-fuel operation (MFO), and ramp-rate (RR) restrictions. In this study, the generation cost and emissions are taken into consideration as objective functions. Since the MAED problem in the power system is inherently nonlinear, adding the aforementioned restrictions makes the problem even more challenging. To address the complexity of the multi-objective optimization problem, the modified grasshopper optimization (MGO) algorithm, based on the chaos mechanism, is proposed in this paper. The proposed method has been tested on a four-area power system with sixteen electrical generators, and the results are contrasted with those of previous evolutionary techniques. Based on the results, it can be concluded that using the proposed MGO method to solve the MAED and RCMAED problems will result in generation costs that are around $300 and $600 less than using the MPSO and PSO methods, respectively. Also, the proposed MGO method has reduced emission levels by roughly 30% as compared to the GO method in order to solve the RCMAEED problem. Full article

Metaheuristic optimization algorithms are tools based on mathematical concepts that are used to solve complicated optimization issues. These algorithms are intended to locate or develop a sufficiently good solution to an optimization issue, particularly when information is sparse or inaccurate or computer capability is restricted. Power systems play a crucial role in promoting environmental sustainability by reducing greenhouse gas emissions and supporting renewable energy sources. Using metaheuristics to optimize the performance of modern power systems is an attractive topic. This research paper investigates the applicability of several metaheuristic optimization algorithms to power system challenges. Firstly, this paper reviews the fundamental concepts of metaheuristic optimization algorithms. Then, six problems regarding the power systems are presented and discussed. These problems are optimizing the power flow in transmission and distribution networks, optimizing the reactive power dispatching, optimizing the combined economic and emission dispatching, optimal Volt/Var controlling in the distribution power systems, and optimizing the size and placement of DGs. A list of several used metaheuristic optimization algorithms is presented and discussed. The relevant results approved the ability of the metaheuristic optimization algorithm to solve the power system problems effectively. This, in particular, explains their wide deployment in this field. Full article

The renewable energy sources (RESs)-based economic dispatch problem (EDP) is of vital importance for modern power systems. Environmental pollution, climatic degradation, and rapidly growing prices of continuously depleting fossil fuels have encouraged researchers to consider mechanisms for RES implementation and optimal operations. This paper presents a quasi-oppositional population-based global particle swarm optimizer with inertial weights (QPGPSO-ω) to solve environment friendly EDPs. The optimization technique is applied to solve the EDP under different scenarios including cases where only renewable energy sources (RESs) are used and the cases where combined emission–economic dispatch (CEED) problem is taken into account. The scenario for RESs includes a combination of six wind, five solar PV, and four biofuel systems for power generation. EDPs are considered without any constraints, and the variability of resources is depicted over time, along with the regional load-sharing dispatch (RLSD). The case of CEED considers ten thermal units with the valve point loading (VPL) effect and transmission losses. The results obtained by the proposed QPGPSO-ω algorithm are better than the reported results employing other optimization methods. This is shown by the lower costs achieved up to USD 8026.1439 for the case of only RES-based EDPs, USD 1346.8 for the case of RES-based EDPs with RLSD, and USD 111,533.59 for the case of CEED. Thus, the proposed QPGPSO-ω algorithm was effective in solving the various adopted power dispatch problems in power system. Full article

As social and environmental issues become increasingly serious, both fuel costs and environmental impacts should be considered in the cogeneration process. In recent years, combined heat and power economic emission dispatch (CHPEED) has become a crucial optimization problem in power system management. In this paper, a novel reinforcement-learning-based multi-objective differential evolution (RLMODE) algorithm is suggested to deal with the CHPEED problem considering large-scale systems. In RLMODE, a Q-learning-based technique is adopted to automatically adjust the control parameters of the multi-objective algorithm. Specifically, the Pareto domination relationship between the offspring solution and the parent solution is used to determine the action reward, and the most-suitable algorithm parameter values for the environment model are adjusted through the Q-learning process. The proposed RLMODE was applied to solve four CHPEED problems: 5, 7, 100, and 140 generating units. The simulation results showed that, compared with four well-established multi-objective algorithms, the RLMODE algorithm achieved the smallest cost and smallest emission values for all four CHPEED problems. In addition, the RLMODE algorithm acquired better Pareto-optimal frontiers in terms of convergence and diversity. The superiority of RLMODE was particularly significant for two large-scale CHPEED problems. Full article

A smart hybrid energy system (SHES) is presented using a combination of battery, PV systems, and gas/diesel engines. The economic/environmental dispatch optimization algorithm (EEDOA) is employed to minimize the total operating cost or total CO₂ emission. In the face of the uncertainty of renewable power generation, the constraints for loss-of-load probability (LOLP) and the operating reserve for the rechargeable battery are taken into account for compensating the imbalance between load demand and power supplies. The grid-connected and islanded modes of SHES are demonstrated to address a low-carbon community. For forecasting load demand, PV power, and locational-based marginal pricing (LBMP), the proper forecast model, such as long short-term memory (LSTM) or extreme gradient boosting (XGBoost), is implemented to improve the EEDOA. A few comparisons show that (i) the grid-connected mode of SHES is superior to the islanded-connected mode of SHES due to lower total operating cost and less total CO₂-eq emissions, and (ii) the forecast-assisted EEDOA could effectively reduce total operating cost and total CO₂-eq emissions of both modes of SHES as compared to no forecast-assisted EEDOA. Full article

Hybrid concentrated solar thermal power (CSP) and photovoltaic (PV) plants are gaining relevance because they combine their advantages: easy installation and low cost of PV plus dispatchability of CSP. This paper presents results of a techno-economic modelling of this hybrid approach for sites in Jordan, Tunisia and Algeria. Local boundary conditions such as meteorology, cost and electricity demand have been considered to determine the best configurations for these three sites. Different CSP technologies with thermal energy storage have been selected. Hybridization with natural gas has also been included. The optimization is done towards minimizing the LCOE while covering the electrical demand 24/7. Results are presented for different CO₂ emissions ranges, as the use of fossil fuel has a strong impact on the LCOE and for environmental reasons, it may be preferred to be kept to a minimum. For most of the cases analyzed, the fraction of energy from PV that leads to minimum LCOE is lower than the energy from CSP. It is shown that for countries with a high fuel price, the use of natural gas reduces the LCOE until a share from this source of about 20%. A higher integration of fossil fuel for sites rich in solar irradiation is considered not advantageous if the price of natural gas is above EUR 40/MWh. Full article