Search Results (721)

The global power sector faces the critical challenge of balancing rising electricity demand with stringent carbon reduction targets. Taiwan’s unique geopolitical and energy import constraints provide an ideal context for exploring advanced grid technologies integrated with carbon-trading mechanisms. This study combines the Adaptive Time-Varying Gravitational Search Algorithm (ATGA) with Static Synchronous Series Compensator (SSSC) technology to optimize power flow and enable carbon transactions between the power generation and transmission sectors. Through a feedback-driven mechanism, power producers acquire carbon credits from transmission operators, maximizing profitability while meeting emission targets. Managed by the transmission companies, the SSSC enhances grid stability, reduces transmission losses, and generates valuable carbon credits. Simulations based on Taiwan’s power market demonstrate that this integrated approach achieves a 50% reduction in emissions and increases profitability for power producers by up to 20%. This model has potential applications in other regions, and future work could explore its scalability and adaptability in different economic and regulatory contexts. Full article

Nuclear microreactors are a potential technology to provide heat and electricity for remote microgrids. There is potential for the microgrid on the island of El Hierro to use a microreactor, within an integrated energy system (IES), to generate electricity and provide desalinated water. This work proposes a workflow for optimizing and analyzing IESs for microgrids. In this study, an IES incorporating a microreactor, thermal energy storage (TES) system, combined heat and power plant, and a thermal desalination plant was designed, optimized, and analyzed using Idaho National Laboratory’s Framework for Optimization of Resources and Economics (FORCE) toolset. The optimization tool, Holistic Energy Resource Optimization Network (HERON), was used to determine the optimal capacity sizes and dispatch for the reactor and thermal energy storage systems to meet demand. The optimized reactor and TES sizes were found to be 11.61 MW_th and 58.47 MWh_th, respectively, when optimizing the IES to replace 95% of the island’s existing diesel generation needs. A dynamic model of the system was created in the Modelica language, using models from the HYBRID repository, to analyze and verify the dispatch from the optimizer. The dynamic model was able to meet the ramp rates while maintaining reactor power with minimal control adjustments. Full article

This paper addresses the critical challenge of scheduling optimization in regional integrated energy systems, characterized by the coupling of multiple physical energy streams (electricity, heat, and cooling) and the participation of various stakeholders. To tackle this, a novel multi-load and multi-type integrated demand response model is proposed, which fully accounts for the heterogeneous characteristics of energy demands in different campus environments. A leader–follower two-layer game equilibrium model is introduced, where the system operator acts as the leader, and campus load aggregators, energy storage plants, and wind farm operators serve as followers. The layer employs an enhanced particle swarm optimization (PSO) algorithm to iteratively adjust energy sales prices and response compensation unit prices, influencing the user response plan through the demand response model. In the lower layer, the charging and discharging schedules of energy storage plants, wind farm energy supply, and outputs of energy conversion devices are optimized to guide system operation. The novelty of this approach lies in the integration of a game-theoretic framework with advanced optimization techniques to balance the interests of all participants and enhance system coordination. A case study is conducted to evaluate the effectiveness of the proposed strategy, demonstrating significant economic benefits. The results show that the model encourages stakeholders to invest in energy infrastructure and actively participate in coordinated dispatch, leading to improved overall system efficiency and comprehensive revenue enhancement for the multi-agent energy system. Full article

The Growth Optimizer algorithm (GO) is a novel metaheuristic that draws inspiration from people’s learning and introspection processes as they progress through society. Economic Load Dispatch (ELD), one of the primary problems in the power system, is resolved by the GO. To assess GO’s dependability, its performance is contrasted with a number of methods. These techniques include the Rime-ice algorithm (RIME), Grey Wolf Optimizer (GWO), Elephant Herding Optimization (EHO), and Tunicate Swarm Algorithm (TSA). Also, the GO algorithm has the competition of other literature techniques such as Monarch butterfly optimization (MBO), the Sine Cosine algorithm (SCA), the chimp optimization algorithm (ChOA), the moth search algorithm (MSA), and the snow ablation algorithm (SAO). Six units for the ELD problem at a 1000 MW load, ten units for the ELD problem at a 2000 MW load, and twenty units for the ELD problem at a 3000 MW load are the cases employed in this work. The standard deviation, minimum fitness function, and maximum mean values are measured for 30 different runs in order to evaluate all methods. Using the GO approach, the ideal power mismatch values of 3.82627263206814 × 10⁻¹², 0.0000622209480241054, and 5.5893360695336 × 10⁻⁷ were found for six, ten, and twenty generator units, respectively. The GO’s dominance over all other algorithms is demonstrated by the results produced for the ELD scenarios. Full article

The core function of a microgrid controller is to compute and distribute a set points related to the distributed energy resources and controllable loads to ensure optimal performance. The development of a real-time economic dispatching algorithm that enhances the operation of microgrids, particularly those involving wind, diesel, and storage systems, is the aim of this paper. The proposed algorithm is based on the flexibility envelope concept, which enables efficient, real-time dispatching, without the need for professional optimization software. The main objective of this paper is to provide a cost-effective and practical solution for managing uncertainties in terms of renewable energy generation and load demand. The algorithm is tested on a microgrid energy management system, in both grid-connected and islanded operation modes. The results demonstrate that the proposed algorithm achieves significant cost reductions compared to a rule-based myopic policy, while closely approximating the optimal dispatch results obtained from offline professional optimization tools. Full article

As the penetration of distributed renewable energy continues to increase in distribution networks, the traditional scheduling model that the inertia and primary frequency support of distribution networks are completely dependent on the transmission grid will place enormous regulatory pressure on the transmission grid and hinder the active regulation capabilities of distribution networks. To address this issue, this paper proposes a coordinated optimization method for distribution networks and microgrid clusters considering frequency constraints. First, the confidence interval of disturbances was determined based on historical forecast deviation data. On this basis, a second-order cone programming model for distribution networks with embedded frequency security constraints was established. Then, microgrids performed economic dispatch considering the reserves requirement to provide inertia and primary frequency support, and a stochastic optimization model with conditional value-at-risk was built to address uncertainties. Finally, a cooperative game between the distribution network and microgrid clusters was established, determining the reserve capacity provided by each microgrid and the corresponding prices through Nash bargaining. The model was further transformed into two sub-problems, which were solved in a distributed manner using the ADMM algorithm. The effectiveness of the proposed method in enhancing the operational security and economic efficiency of the distribution networks is validated through simulation analysis. Full article

The rapid advancement of battery technology has drawn attention to the effective dispatch of distributed battery storage systems. Batteries offer significant benefits in flexible energy supply and grid support, but maximising their cost-effectiveness remains a challenge. A key issue is balancing conflicts between intentional network services, such as energy arbitrage to reduce the overall electricity costs, and unintentional services, like fault-induced unintentional islanding. This paper presents a novel dispatch methodology that addresses these conflicts by considering both energy arbitrage and unintentional islanding services. First, demand profiles are clustered to reduce uncertainty, and uncertainty sets for photovoltaic (PV) generation and demand are derived. The dispatch strategy is originally formulated as a robust optimal power flow problem, accounting for both economic benefits and risks from unresponsive islanding requests, alongside energy loss reduction to prevent a battery-induced artificial peak. Last, this paper updates the objective function for adapting possible long-run competition changes. The IEEE 33-bus system is utilised to validate the methodology. Case studies show that, by considering the reserve for possible islanding requests, a battery with limited capacity will start to discharge after a demand drop from the peak, leading to the profit dropping from USD 185/day (without reserving capacity) to USD 21/day. It also finds that low-resolution dynamic pricing would be more appropriate for accommodating battery systems. This finding offers valuable guidance for pricing strategies. Full article

With the load center’s continuous expansion and development of the AC power grid’s scale and construction, the recipient grid under the multi-feed DC environment is facing severe challenges of DC commutation failure and bipolar blocking due to the high strength of AC-DC coupling and the low level of system inertia, which brings many complexities and uncertainties to economic scheduling. In addition, the large-scale grid integration of wind power, photovoltaic, and other intermittent energy sources makes the ultra-high-voltage (UHV) DC channel operation state randomized. The deterministic scenario-based timing power simulation is no longer suitable for the current complex and changeable grid operation state. In this paper, we first start with the description and analysis of the uncertainty in renewable energy (RE) sources, such as wind and solar, and reconstruct the time-sequence power model by using the stochastic differential equation model. Then, a carbon emission trading cost (CET) model is constructed based on the CET mechanism, and the two-stage locating and capacity optimization model for the UHV DC receiving end is proposed under the constraint of dispatch safety and stability. Among them, the first stage starts with the objective of maximizing the carrying capacity of the UHV DC receiving end grid; the second stage checks its dynamic safety under the basic and fault modes according to the results of the first stage and corrects the drop point and capacity of the UHV DC line with the objective of achieving safe and stable UHV DC operation at the lowest economic investment. In addition, the two-stage model innovatively proposes UHV DC relative inertia constraints, peak adjustment margin constraints, transient voltage support constraints under commutation failure conditions, and frequency support constraints under a DC blocking state. In addition, to address the problem that the probabilistic constraints of the scheduling model are difficult to solve, the discrete step-size transformation and convolution sequence operation methods are proposed to transform the chance-constrained planning into mixed-integer linear planning for solving. Finally, the proposed model is validated with a UHV DC channel in 2023, and the results confirm the feasibility and effectiveness of the model. Full article

Energy storage (ES) units are vital for the reliable and economical operation of the power system with a high penetration of renewable distributed generators (DGs). Due to ES’s high investment costs and long payback period, energy management with shared ESs becomes a suitable choice for the demand side. This work investigates the sharing mechanism of ES units for low-voltage (LV) energy prosumer (EP) communities, in which energy interactions of multiple styles among the EPs are enabled, and the aggregated ES dispatch center (AESDC) is established as a special energy service provider to facilitate the scheduling and marketing mechanism. A shared ES operation framework considering multiple EP communities is established, in which both the energy scheduling and cost allocation methods are studied. Then a shared ES model and energy marketing scheme for multiple communities based on the leader–follower game is proposed. The Karush–Kuhn–Tucker (KKT) condition is used to transform the double-layer model into a single-layer model, and then the large M method and PSO-HS algorithm are used to solve it, which improves convergence features in both speed and performance. On this basis, a cost allocation strategy based on the Owen value method is proposed to resolve the issues of benefit distribution fairness and user privacy under current situations. A case study simulation is carried out, and the results show that, with the ES scheduling strategy shared by multiple renewable communities in the leader–follower game, the energy cost is reduced significantly, and all communities acquire benefits from shared ES operators and aggregated ES dispatch centers, which verifies the advantageous and economical features of the proposed framework and strategy. With the cost allocation strategy based on the Owen value method, the distribution results are rational and equitable both for the groups and individuals among the multiple EP communities. Comparing it with other algorithms, the presented PSO-HS algorithm demonstrates better features in computing speed and convergence. Therefore, the proposed mechanism can be implemented in multiple scenarios on the demand side. Full article

Battery Energy Storage Systems (BESSs) play a vital role in modern power grids by optimally dispatching energy according to the price signal. This paper proposes a reinforcement learning-based model that optimizes BESS scheduling with the proposed Q-learning algorithm combined with an epsilon-greedy strategy. The proposed epsilon-greedy strategy-based Q-learning algorithm can efficiently manage energy dispatching under uncertain price signals and multi-day operations without retraining. Simulations are conducted under different scenarios, considering electricity price fluctuations and battery aging conditions. Results show that the proposed algorithm demonstrates enhanced economic returns and adaptability compared to traditional methods, providing a practical solution for intelligent BESS scheduling that supports grid stability and the efficient use of renewable energy. Full article

The increase in new energy grid connections has reduced the supply-side regulation capability of the power system. Traditional economic dispatch methods are insufficient for addressing the flexibility limitations in the system’s balancing capabilities. Consequently, this study presents a day-ahead scheduling method for renewable energy systems that balances flexibility and economy. This approach establishes a dual-layer optimized scheduling model. The upper-layer model focuses on the economic efficiency of unit start-up and shut-down, utilizing a particle swarm algorithm to identify unit combinations that comply with minimum start-up and shut-down time constraints. In contrast, the lower-layer model addresses the dual uncertainties of generation and load. It employs the Generalized Polynomial Chaos approximation to create an opportunity-constrained model aimed at minimizing unit generation and curtailment costs while maximizing flexibility supply capability. Additionally, it calculates the probability of flexibility supply-demand insufficiency due to uncertainties in demand response resource supply and system operating costs, providing feedback to the upper-layer model. Ultimately, through iterative solutions of the upper and lower models, a day-ahead scheduling plan that effectively balances flexibility and economy is derived. The proposed method is validated using a simulation of the IEEE 30-bus system case study, demonstrating its capability to balance system flexibility and economy while effectively reducing the risk of insufficient supply-demand balance. Full article

Electrical power systems are the lifeblood of modern civilization, providing the essential energy infrastructure that powers our homes, industries, and technologies. As our world increasingly relies on electricity, and modern power systems incorporate renewable energy sources, the challenges have become more complex, necessitating advanced forecasting and optimization to ensure effective operation and sustainability. This review paper provides a comprehensive overview of electrical power systems and delves into the crucial roles that forecasting and optimization play in ensuring future sustainability. The paper examines various forecasting methodologies from traditional statistical approaches to advanced machine learning techniques, and it explores the challenges and importance of renewable energy forecasting. Additionally, the paper offers an in-depth look at various optimization problems in power systems including economic dispatch, unit commitment, optimal power flow, and network reconfiguration. Classical optimization methods and newer approaches such as meta-heuristic algorithms and artificial intelligence-based techniques are discussed. Furthermore, the review paper examines the integration of forecasting and optimization, demonstrating how accurate forecasts can enhance the effectiveness of optimization algorithms. This review serves as a reference for electrical engineers developing sophisticated forecasting and optimization techniques, leading to changing consumer behaviors, addressing environmental concerns, and ensuring a reliable, efficient, and sustainable energy future. Full article

The dispatching of trackless transportation on the ramp of underground metal mines is closely related to the transportation efficiency of daily production equipment, personnel, and construction materials in the mine. The current dispatching of trackless transportation on the ramp of underground metal mines is discontinuous and imprecise, with unscientific vehicle arrangement leading to low efficiency and transportation congestion. This paper presents this study, which puts forward a kind of trackless transportation optimization method that can fully make use of the ramp in the roadway, and the slow slope fork point can be used for the trackless transportation vehicle passing section to improve the efficiency of trackless transportation on the ramp. This study adopts the principles of fuzzy logic and uses interval-based positioning instead of real-time positioning to effectively reduce the spatial complexity inherent in the algorithm. At the same time, this research presents a modified genetic algorithm that incorporates a time-loss fitness calculation. This innovation makes it possible to differentiate traffic priorities between different types of vehicles, thus bringing the scheduling scheme more in line with the economic objectives of the mining operations. Various parameters were determined and several sets of simulation experiments were carried out on the response speed and scheduling effect of the scheduling system, resulting in a 10 to 20 percent improvement for different vehicles in the efficiency of underground mining transport operations. Full article

In electrical power system engineering, the economic load dispatch (ELD) problem is a critical issue for fuel cost minimization. This ELD problem is often characterized by non-convexity and subject to multiple constraints. These constraints include valve-point loading effects (VPLEs), generator limits, emissions, and wind power integration. In this study, both emission constraints and wind power are incorporated into the ELD problem formulation, with the influence of wind power quantified using the incomplete gamma function (IGF). This study proposes a novel metaheuristic algorithm, the modified moth flame optimization (MMFO), which improves the traditional moth flame optimization (MFO) algorithm through an innovative flame selection process and adaptive adjustment of the spiral length. MMFO is a population-based technique that leverages the intelligent behavior of flames to effectively search for the global optimum, making it particularly suited for solving the ELD problem. To demonstrate the efficacy of MMFO in addressing the ELD problem, the algorithm is applied to four well-known test systems. Results show that MMFO outperforms other methods in terms of solution quality, speed, minimum fuel cost, and convergence rate. Furthermore, statistical analysis validates the reliability, robustness, and consistency of the proposed optimizer, as evidenced by the consistently low fitness values across iterations. Full article

In traditional short-term hydropower scheduling problems, which usually determine the optimal power generation schedules within one week, the off-design zone of a hydraulic turbine is modeled as a forbidden zone due to the significantly increased risk of turbine damage when operating within this zone. However, it is still plausible to occasionally operate within this zone for short durations under real-world circumstances. With the integration of Variable Renewable Energy (VRE) into the power system, hydropower, as a dispatchable energy resource, operates complementarily with VRE to smooth overall power generation and enhance system performance. The rapid and frequent adjustments in output power make it inevitable for the hydraulic turbine to operate in the off-design zone. This paper introduces the operating zones associated with various production costs derived from fatigue analysis of the hydraulic turbine and calculated based on the turbine replacement cost. These costs are incorporated into a short-term hybrid scheduling tool based on Mixed Integer Linear Programming (MILP). Including production costs in the optimization problem shifts the turbine’s working point from a high-cost zone to a low-cost zone. The resulting production schedule for a Hydro-Solar hybrid power system considers not only short-term economic factors such as day-ahead market prices and water value but also lifetime hydraulic turbine cost, leading to a more comprehensive calculation of the production plan. This research provides valuable insights into the sustainable operation of hydropower plants, balancing short-term profits with lifetime hydraulic turbine costs. Full article