Minimum System Cost Research Articles

Mobile Acoustic Net: A novel early detection model for wood-boring pests

Wood-boring pests (WBPs) are among the most destructive pests to trees, yet their presence is difficult to detect due to their concealed lifestyles. Audio signals generated through the activities of WBPs can act as evidence for their existence. This paper presents a novel lightweight CNN (Convolutional Neural Network), named Mobile-Acoustic Net (MAnet) which features efficient structures and utilizes audio signals to detect the early presence of WBPs. A public benchmark dataset is expanded by combining pure sounds of WBPs and pure environment sounds. Two audio features, namely Mel Frequency Cepstral Coefficient (MFCC) and Linear Frequency Cepstral Coefficient (LFCC), were extracted from the original audios and served as inputs to MAnet. To better utilize these input features, a novel deep fusion module was designed to generate fusion features. To decrease the model computation, Blueprint Separable Convolutions were employed to replace the classical convolution operations. Efficient Channel Attention was added with minimal system cost to enhance model performance. The decoupled knowledge distillation was employed to train the trimmed model, resulting in a smaller version of MAnet that maintains similar recognition performance. Experiments have shown that MAnet achieved better performance and smaller size compared to other models, with an average accuracy of 96.136 % and a minimum computation of 8.811 M, demonstrating its effectiveness. MAnet can provide a new way of detection for WBPs, which contributes to forest protection and pest management.

Multi-port network based modeling and selection of capacitor for desired voltage regulation of a standalone six-phase short-shunt induction generator for application in remote areas

This paper gives a straightforward method to determine the values of excitation capacitors of a standalone short-shunt six-phase induction generator (SPIG) to maintain the voltage profile within predetermined percentage of voltage deviation (VD). In this envisioned study, the value of the capacitor is meticulously chosen to optimize the number of capacitor switching, ensuring minimal system cost and complexity. The theory of multi-port network analysis has been applied for modelling of the SPIG, thus, the complex mathematical derivation to obtain the model equations is avoided. The system is expressed as a multivariable nonlinear optimization problem. The resultant admittance of the SPIG is calculated from its per phase equivalent circuit and is used as an objective function, which is solved using Binary Search Algorithm (BSA). The main novelty of this work is the determination of the model equations of the SPIG system in an efficient and simple way using the multi-port network analysis approach. Along with this, the BSA is employed for optimal selection of excitation capacitors because of its simplicity and less computational time. The results, on a 3.7 kW induction machine, reveal that to maintain a 4 % VD, a fixed series capacitor of 140 µF and two switched shunt capacitors (34.4 µF, 91.8 µF) are required. For 2 % VD, four shunt capacitors (24.2µF, 36.2µF, 64.7µF, 91.2µF) are necessary. The performance of the machine is evaluated with the help of magnetic characteristics and other equations obtained from its per phase equivalent circuit. The experimentation has been carried out in a hardware prototype system developed in the laboratory. The experimental and the simulated results are compared and found that both are nearly same for different operating conditions, which indicates the efficacy of the proposed approach.

Optimum Planning of Carbon Capture and Storage Network Using Goal Programming

Carbon capture and storage (CCS) is a critical technology used for mitigating climate change by capturing carbon dioxide emissions from industrial sources and storing them underground to prevent their release into the atmosphere. Despite its potential, optimizing CCS systems for cost-effectiveness and efficiency improvement remains a significant challenge. In this paper, the optimization of CCS systems through the development and application of two mathematical optimization techniques is introduced. The first technique is based on using a superstructure optimization model, while the second technique relies on applying a goal programming optimization model. These models were solved using LINGO software version API 14.0.5099.166 to enhance the efficiency and cost-effectiveness of CCS systems. The first model, seeking to maximize the exchange of CO2 flowrate from sources to sinks, achieved a CO2 capture rate of 93.36% with an annual total cost of USD 1.175 billion. The second model introduced a novel mixed-integer non-linear programming (MINLP) approach for multi-objective optimization, targeting the minimization of total system cost, alternative storage, and unutilized storage while maximizing CO2 load exchange. The application of the second model, when prioritized to maximize CO2 flowrate exchange using the goal programming technique, resulted in a cost reduction of 36.46% and a CO2 capture rate of 75.87%. In contrast, when the second model prioritized minimizing the total annual cost, a 48% cost reduction was achieved, and the CO2 capture rate was decreased by 68.37%. A comparison of the two models’ results is presented. The results showed that the second model, with the priority of maximizing CO2 capture, provides the best economic–environmental objective balance, which offers notable cost reductions while keeping an efficient CO2 capture rate. This study highlights the potential of advanced mathematical modeling in increasing the feasibility of CCS as one of the very important strategies of mitigating climate change and reducing global warming.

MANGOever: An optimization framework for the long-term planning and operations of integrated electric vehicle and building energy systems

The growing electrification of heating and mobility has increased the interdependence of these two sectors and introduced a new coupling with the electricity sector. However, existing studies on local energy planning often focus solely on solutions to meet buildings’ energy demands, neglecting or highly simplifying new mobility demands. Here, we address this gap by introducing MANGOever (Multi-stAge eNerGy Optimization for electric vehicles and energy retrofits), a comprehensive optimization framework for long-term co-planning of building energy systems and electric vehicle (EV) charging infrastructure. The framework optimizes multi-stage investments and operational strategies to minimize system costs and CO2 emissions over a multi-year horizon, considering the stochastic nature of EV charging based on observed driver habits and travel patterns. Applying the model to a case study of a multi-family home in Switzerland reveals significant synergies between EV charging and the management of solar photovoltaic generation. The results underscore the importance of considering habit-based EV charging behavior in the model and demonstrate how diverse EV plug-in behaviors can be leveraged to maximize the use of midday solar production and reduce emissions. These findings emphasize the need for integrated planning of these sectors to achieve a cost-effective, low-carbon energy transition.

A distributionally robust optimization approach of multi-park integrated energy systems considering shared energy storage and Uncertainty of Demand Response

To enhance the economic efficiency and renewable energy integration capacity of multi-park integrated energy systems (MPIES) and address the issue of insufficient consideration of demand response uncertainty in existing studies, this paper proposes a distributionally robust optimization approach for multi-park integrated energy systems, considering shared energy storage and the uncertainty of demand response. First, models of the shared energy system and demand response are established. Based on these models, a deterministic optimization scheduling model for MPIES is developed, aiming to minimize system costs while considering constraints such as grid power balance. To address uncertainty in demand response, this paper employs Interval Type-2 Fuzzy Theory to construct uncertainty sets and considers system reliability constraints. The original optimization problem is then transformed into an equivalent robust counterpart model, and the optimal distributionally robust solution is obtained through parameter domain decomposition methods. Finally, the proposed method is validated using the IEEE 33-bus system. The results show that considering shared energy storage and demand response individually can reduce total system costs by 4.86 % and 26.46 %, respectively. After accounting for the uncertainty in demand response, the total system cost increases only slightly by 4.36 %, but this improves the system's robustness.

An on-demand collaborative edge caching strategy for edge–fog–cloud environment

In this paper, we tackle the critical challenges of content edge caching, such as limited storage capacity, content popularity prediction, dynamic user demand, and user privacy, issues that most existing studies only address partially. We present an innovative Genetic Algorithm-based On-demand Collaborative Edge Caching mechanism (GAOCEC), which introduces a multi-tiered caching architecture integrating cloud, fog, and edge computing. To enhance caching efficiency and minimize system cost, a novel on-demand caching quota mechanism is proposed that dynamically allocates cache resources to edge servers. To strengthen user privacy protection during content popularity prediction, a CNN-BiLSTM-based Federated Learning algorithm (CBFL) is presented that ensures high prediction accuracy without the need to upload local data to the cloud. We also refine the genetic algorithm for content placement by fine-tuning various parameter sets to identify the optimal balance between latency reduction and caching cost. Our experimental results validate the effectiveness of our approach, demonstrating increased cache hit rates, decreased content response times, and an overall improvement in system efficiency. This work provides a comprehensive, adaptive, and privacy-preserving solution for the edge–fog–cloud environment.

Optimal power management of a stand-alone hybrid energy management system: Hydro-photovoltaic-fuel cell

The field of electrical engineering with renewable energy systems has witnessed a remarkable improvement due to an increase in the world's growing population as available resources become limited. This study proposes an optimal and efficient hybrid energy management system that combines photovoltaic, hydro, and fuel cell renewable energy resources that can solve the drawbacks of current energy setups and offer a dependable power source for stand-alone purposes. This novel AC-linked hybrid energy system features a comprehensive power management strategy with actual weather data and a realistic load demand profile. The HOMER Pro technique is employed for calculating the emission of toxic gases, system economic benefits, sensitivity analysis and cost minimization of the system while providing sustainability. The numerical outcomes show optimal results as compared to the existing systems in the literature. The system consists of 55 kW of PV modules, a 30 kW FC generator, and 40 kW of hydropower, suggesting optimal and excellent economic and environmental sustainability. This system appeared to be an especially cost-effective solution, with a net present cost (NPC) of $248,773 and a cost of energy (COE) of $0.0546/kWh. The results show that the system can meet energy needs while utilising the available resources efficiently in different weather conditions. The proposed system has the potential to increase the sustainability and dependability of power generation in a variety of applications and aid in the design and implementation of alternative energy systems, particularly in off-grid or remote areas. The proposed system can be extended to other such locations that will provide a scalable and flexible solution for electricity generation, making it suitable for a variety of applications, from small rural power plants to large industrial power supplies. Overall, the proposed study provides an important resource for development in hybrid energy system design and operation, with far-reaching implications for the transition to a sustainable and low-carbon future.

Cost-effective task offloading and trajectory optimization in UAV assisted edge networks with DDPG

PurposeUnmanned aerial vehicles (UAVs), known for their exceptional flexibility and maneuverability, have become an integral part of mobile edge computing systems in edge networks. This paper aims to minimize system costs within a communication cycle. To this end, this paper has developed a model for task offloading in UAV-assisted edge networks under dynamic channel conditions. This study seeks to efficiently execute task offloading while satisfying UAV energy constraints, and validates the effectiveness of the proposed method through performance comparisons with other similar algorithms.Design/methodology/approachTo address this issue, this paper proposes a task offloading and trajectory optimization algorithm using deep deterministic policy gradient, which jointly optimizes Internet of Things (IoT) device scheduling, power distribution, task offloading and UAV flight trajectory to minimize system costs.FindingsThe analysis of simulation results indicates that this algorithm achieves lower redundancy compared to others, along with reductions in task size by 22.8%, flight time by 34.5%, number of IoT devices by 11.8%, UAV computing power by 25.35% and the required cycle for per-bit tasks by 33.6%.Originality/valueA multi-objective optimization problem is established under dynamic channel conditions, and the effectiveness of this approach is validated.

Iterative Root-Finding Algorithm for Accurate Parameter Extraction of Solar Photovoltaic Cells

The performance of photovoltaic models depends significantly on the accuracy of their parameters, which are determined by the chosen method and objective function. Extracting these parameters accurately under different environmental conditions is essential to enhance reliability, accuracy, and minimize system costs. In this research, a novel technique is proposed for extracting the electrical parameters of the solar cell single diode model, including saturation current, serial resistance, parallel resistance, and ideality factor. To overcome the challenges posed by the chaotic behavior of the I-V curve equation, an improved Iterative Root-Finding algorithm is introduced. This algorithm acts as an optimization tool, increasing the likelihood of obtaining highly accurate solutions by minimizing the quadratic error between experimental and theoretical characteristics in a shorter time frame. The numerical and experimental results demonstrate the effectiveness of this approach in solar module modeling, showing squared errors approaching zero. This study opens new possibilities for improving the accuracy and reliability of photovoltaic models, leading to more efficient solar energy systems.

Combined effect of climate change and labor relation on optimally managing labor productivity under uncertainty

Heat stress amplified by climate change causes excessive reductions in labor capacity, work injuries, and socio-economic losses. Yet studies of corresponding impact assessments and adaptation developments are insufficient and incapable of effectively dealing with uncertain information. This gap is caused by the inability to resolve complex channels involving climate change, labor relations, and labor productivity. In this paper, an optimization-based productivity restoration modeling framework is developed to bridge the gap and support decision-makers in making informed adaptation plans. The framework integrates a multiple-climate-model ensemble, an empirical relationship between heat stress and labor capacity, and an inexact system costs model to investigate underlying uncertainties associated with climate and management systems. Optimal and reliable decision alternatives can be obtained by communicating uncertain information into the optimization processes and resolving multiple channels. Results show that the increased heat stress will lead to a potential reduction in labor productivity in China. By solving the objective function of the framework, total system costs to restore the reduction are estimated to be up to 248,700 million dollars under a Representative Concentration Pathway of 2.6 (RCP2.6) and 697,073 million dollars under RCP8.5 for standard employment, while less costs found for non-standard employment. However, non-standard employment tends to restore productivity reduction with the minimum system cost by implementing active measures rather than passive measures due to the low labor costs resulting from ambiguities among employment statuses. The situation could result in more heat-related work injuries because employers in non-standard employment can avoid the obligation of providing a safe working environment. Urgent actions are needed to uphold labor productivity with climate change, especially to ensure that employers from non-standard employment fulfill their statutory obligations.

Dynamic Calculation Method for Zonal Carbon Emissions in Power Systems Based on the Theory of Production Simulation and Carbon Emission Flow Theory

Power systems are the main source of carbon emissions. Currently, coordinated operation strategies of the source–grid–load–storage model considering carbon emissions is primarily expanded from the generation side. For practical power systems, where multiple types of generating units coexist at a single node, it is difficult to develop unit combination strategies that simultaneously consider carbon emission factors and power flow constraints. Therefore, a new power flow calculation method based on connectivity matrix theory was proposed, aiming to address the issues of existing approaches that are too coarse and unable to accurately represent the operating states of multiple units under each node. Furthermore, a new method for dynamic calculation of regional carbon emission based on connectivity matrix and carbon emissions flow was introduced to improve the accuracy of carbon emission measurements. Firstly, a simulation model for a coordinated optimization operation based on the minimum system cost for the source–grid–load was established and an optimal flow calculation method using a connectivity matrix was introduced. Second, a dynamic carbon emission calculation method, considering electricity sources, was developed by combining the results of the optimal power flow calculation with carbon emission flow theory. Finally, the effectiveness of the approach in this article was verified by the IEEE 14-bus system example and a provincial power grid, ensuring strict adherence to the conservation principle of carbon emissions between the supply and demand sides and satisfying power flow constraints.

Optimal design of a combined Power-Heating-Oxygen system for plateau hotel buildings

The Qinghai–Tibet Plateau of China is a region of low atmospheric pressure and oxygen deficiency, with a high heating demand in the winter; therefore, plateau hotel buildings have a demand for comprehensive power-heating-oxygen loads. Solar energy resources are particularly abundant in plateau areas, where a combined multi-energy system based on solar energy can be designed to meet the demand of plateau hotel buildings for multiple loads. However, currently, there is a relative lack of methods for the optimal design and performance evaluation of combined power-heating-oxygen (CPHO) systems. Thus, this study proposes a CPHO system model for plateau hotels, and provides two operation strategies, i.e., power priority and oxygen priority. Taking the minimum total annual cost of the combined system as the optimization objective, an optimized CPHO system model for plateau hotels has been proposed, followed by a case study of a hotel in Qilian County, Qinghai Province. Results showed that: 1) Compared with the conventional combined power-heating (CCPH) system, the CPHO system saw reductions of 0.8% and 1.3% in its total annual cost under the operation strategies of power priority and oxygen priority, respectively. 2) The energy self-consumption (SC) rates of the CCPH system and CPHO system under the operation strategies of power priority and oxygen priority were 84.8%, 64.2%, and 89.8%, respectively; with power self-sufficiency (PSS) rates of 8.3%, 49.9%, and 29.5%, and oxygen self-sufficiency (OSS) rates of 0.0%, 3.2%, and 4.7%, respectively. 3) The total annual cost of the CPHO system was affected slightly by the cost of photovoltaics (PVs) and energy storage devices, and significantly by the price of oxygen. When the price of oxygen was 8 yuan per normal cubic meter(Nm3) or above, the CPHO system was economically efficient under the operation strategy of oxygen priority. When the price of oxygen was 10yuan/Nm3 or above, the system was economically efficient under both operation strategies.

Power system investment optimization to identify carbon neutrality scenarios for Italy

In 2021 the European Commission has proposed the Fit-for-55 policy package, requiring European countries to reduce their CO2 emissions by 55% with respect to 1990 by the year 2030, a first step to achieve carbon neutrality by 2050. Energy system modeling can be a valuable tool for national policymakers to choose the most appropriate technologies to achieve these goals efficiently. This article presents a model of the Italian power system realized employing the open energy modeling framework, Oemof. A linear programming optimization is implemented to evaluate how to minimize system costs at decreasing CO2 emissions in 2030. The developed tool is applied to evaluate different research questions: (i) pathway towards full decarbonization and power self-sufficiency of the electricity sector in Italy, (ii) relevance of flexibility assets in power grids: li-ion batteries, hydrogen storage and transmission lines reinforcement. A 55% CO2 emissions reduction for the actual Italian power sector can be achieved through an increase of 30% of the total annual system cost. Achieving complete decarbonization and self-sufficiency increases significatively annual expenditures. However, cost mitigation is plausible through the integration of sector coupling methodologies or the adoption of a broader spectrum of technological solutions. Flexibility measures appear instrumental for decarbonization, particularly transmission lines, demanding a substantial expansion beyond the stated plans for 2030. This infrastructure is crucial in Italy to facilitate the transfer of renewable electricity generated in the Southern regions to the Northern areas, where a large portion of the electricity demand is located.

Optimal energy management strategy for electric vehicles and electricity distribution system based on quantile deep learning with enhanced African vulture optimization

The increasing fluctuation in electricity prices due to unpredictable demand loads on the power grid has underscored the importance of finding optimal scheduling solutions for electric vehicles (EVs). Integrating EVs into the distribution system (DS) is critical for efficiently managing their energy needs. However, current EV scheduling approaches face challenges. Centralized solutions are often unreliable and complex, while existing decentralized methods struggle to efficiently schedule EVs in large networks with smart energy DS. To address these challenges, we propose a hybrid technique called Quantile Deep Learning with Enhanced African Vulture Optimization (Quantum VultureNet) for energy management systems (EMS) involving EVs and DS. This approach combines the African Vultures Optimization algorithm with elite mutation, learning based on dynamic opposition, and chaotic map-based population initialization. By enhancing exploration and exploitation capabilities, this hybrid method aims to improve the scheduling of EVs, particularly in dynamic environments. The primary goal of the Quantum VultureNet technique is to minimize system costs and power losses while optimizing energy management. The proposed system considers factors such as bidirectional energy trading, EV fleet arrival times in driving plans, Photo Voltaic uncertainty impacts on energy management systems, and factors affecting energy selling back to the grid. The model was implemented in MATLAB and compared with conventional methods. Simulation results demonstrate that the Quantum VultureNet technique effectively finds near-global optimum solutions with reduced computational complexity. This indicates the effectiveness of the proposed approach for EMS in EVs integrated with DS.

Impact of maximized utility benefit based on customer willingness for economic operation of a grid connected microgrid system

Optimal scheduling of distributed energy resources (DERs) to obtain a minimized generation cost of a low voltage (LV) microgrid (MG) system has always gravitated the power system optimization researchers. This paper proposes a dual level optimization framework which emphasizes on maximizing the DISCOM profit in the first level and later minimizes the generation cost of the MG system. The MG system considered for the study consists of renewable energy sources, fossil fueled generators, micro turbine and fuel cell and the cost components involve depreciation cost, fuel cost, penalized emission cost and operation and maintenance (O&M) cost. An incentive based demand response (IBDR) policy is implemented which utilizes customer’s willingness to curtail their load demands and offers incentives for the same. When the DISCOMs share of profit is maximized using IBDR policy in level one, the modified load demand of the MG system obtained by deducting the curtailed load from the forecasted load is considered to minimize the generation cost of the MG system in the second level. A recently developed swift and facile Arithmetic optimization algorithm (AOA) is used as optimization tool for the study. The maximum DISCOM benefit was found to be 211▪ when the customers curtailed 105 kWh energy as per their willingness. Literatures reported the minimum power generation cost of the MG system as 880▪, 871▪ and 807▪ for base load, load shifting based demand side management (DSM) and customer incentive-based DR policies respectively. The proposed IBDR policy outperformed the reported literatures with a minimal generation cost of 768▪. Measures of central tendencies also corroborate to the robustness and efficiency of AOA.

Allocating redundancy, maintenance and spare parts for minimizing system cost under decentralized repairs

Allocating redundancy, maintenance and spare parts for minimizing system cost under decentralized repairs

Optimal Placement and Capacity of BESS and PV in EV Integrated Distribution Systems: The Tenth Feeder of Phitsanulok Substation Case Study

Installing a battery energy storage system (BESS) and renewable energy sources can significantly improve distribution network performance in several aspects, especially in electric vehicle (EV)-integrated systems because of high load demands. With the high costs of the BESS and PV, optimal placement and capacity of them must be carefully considered. This work proposes a solution for determining the optimal placement and capacity of a BESS and photovoltaic (PV) in a distribution system by considering EV penetrations. The objective function is to reduce system costs, comprising installation, replacement, and operation and maintenance costs of the BESS and PV. The replacement cost is considered over 20 years, and the maintenance and operation costs incurred in the distribution system include transmission line loss, voltage regulation, and peak demand costs. To solve the problem, two metaheuristic algorithms consisting of particle swarm optimization (PSO) and the African vulture optimization algorithm (AVOA) are utilized. The tenth feeder of Phitsanulok substation 1 (PLA10), Thailand, which is a 91-bus distribution network, is tested to evaluate the performance of the proposed approach. The results obtained from the considered algorithms are compared based on distribution system performance enhancement, payback period, and statistical analysis. It is found from the simulation results that the installation of the BESS and PV could significantly minimize system cost, improve the voltage profile, reduce transmission line loss, and decrease peak demand. The voltage deviation could be reduced by 86%, line loss was reduced by 0.78 MW, and peak demand could be decreased by 5.706 MW compared to the case without BESS and PV installations.

Research on multiobjective capacity configuration optimization of grid‐connected wind–solar–storage microgrid system based on improved BWO algorithm

AbstractHow to effectively utilize renewable energy and improve the economic efficiency of microgrid system and its ability to consume renewable energy has become one of the main problems facing China at present. In response to this challenge, this paper establishes a multiobjective capacity optimization model with the minimum levelized cost of energy, the maximum proportion of renewable energy consumption, and the minimum comprehensive system cost. Based on this model, a new improved beluga whale optimization algorithm is proposed to solve the multiobjective optimization problem in the capacity allocation process of wind–solar–storage microgrid system with the goal of ensuring that the microgrid can meet the maximum load demand at different moments throughout the year. In this paper, opposition‐based learning, artificial bee colony, dynamic opposite, and beluga whale optimization are combined to improve the population diversity and convergence accuracy, thereby enhancing the optimization performance of the algorithm. Finally, after finding the optimal Pareto front solution, the Technique for Order Preference by Similarity to an Ideal Solution is used to help decision‐makers select the optimal solution. Using real load data and meteorological data, the results of this paper show that the multiobjective capacity allocation optimization method of grid‐connected scenic storage microgrid system based on the improved beluga whale optimization algorithm can improve the economics of the wind–solar–storage microgrid system and promote the photovoltaic consumption simultaneously, providing a solution for the realization of low‐carbon power and regional economic development. The best‐found levelized cost of energy for the wind–solar–storage microgrid system is 0.192 yuan/kWh.

Deep Learning based Efficient Edge Slicing for System Cost Minimization in Wireless Networks

Deep Learning based Efficient Edge Slicing for System Cost Minimization in Wireless Networks

Peak-shaving cost of power system in the key scenarios of renewable energy development in China: Ningxia case study

Renewable energy has developed rapidly in Ningxia, and it has become the first provincial power system in China whose renewable energy power generation output exceeds the power load of the whole region. The access of a high proportion of renewable energy puts forward higher requirements for the peak-shaving capacity of the Ningxia power system. In order to solve the problem of calculating the peak-shaving cost in the key scenarios of renewable energy development in Ningxia, a quantitative model of the peak-shaving cost of the power system considering time-of-use price is proposed. We take Ningxia power system as an example to study. First, the key scenarios of the Ningxia power system peak-shaving are obtained, and the technical cost characteristic boundaries of various peak-shaving resources are determined. Second, according to the characteristics of typical daily renewable energy and load in Ningxia, the quantification model of the power system peak-shaving cost is established. The model takes the minimum total cost of the power system as the objective function and considers the constraints such as technical output of thermal power units, charge-discharge characteristics of energy storage and time-of-use price mechanism. Finally, the model is solved and the peak-shaving cost and unit output under the optimal scheme are obtained. This example shows that the model can effectively evaluate the peak-shaving cost and unit output of the Ningxia power system under the key scenarios of peak-shaving.