Profit Of Owners Research Articles

A comprehensive review of optimal placement of EVCS and sizing in active distribution network

ABSTRACT The transition from Internal Combustion (IC) engines to Electric Vehicles (EVs), the electric load on the distribution system has increased gradually in last few decades. There are various challenges in the development of EVCS infrastructure to give cost effective charging. In this article smart charging system (Grid to Vehicle (G2V), Vehicle to Grid (V2G), Vehicle to Vehicle (V2V) and Mobile Charging Station (MCS)), EV load modeling and the latest optimization method used by the researchers are discussed. EVCS as compensation devices to reduce the effect of EV load on the distribution network along with the safety considerations is also discussed in this article. Further, a Smart Energy management system is discussed to increase the EVCS owner profit. Also, the EVCS have to be sited at optimal place in the grid to reduce the system power losses, ensure stability, improve reliability, etc. and also ensure the EV demands. Suitable integration of (EVCS) at the proper places is challenging and essential to overcome the above issues. In addition, there are some other parameters like cost of EVCS and the behavior of EV which are also important for Distribution network operators and the charging station owners are also discussed in this article.

A multi-objective bi-level framework to model distribution system operator's behavior in the wholesale and local transactive markets

This paper presents a multi-objective bi-level framework to model the distribution system operator's (DSO's) behavior in both wholesale and local electricity markets. The upper level is a multi-objective optimization problem from the DSO point of view including operation cost minimization, flexibility maximization, and peak load minimization. To this end, the DSO could not only participate in the wholesale electricity market at both day-ahead and real-time stages, but also is able to purchase power from renewable generations as well as the private owners. In this regards, the DSO could manage fluctuations of renewable power generations through considering flexibility as an objective function along with cost and peak-shaving. The lower level assigns to the private owner's profit maximization through interactions with the DSO. The DSO also exploits the demand response potential by implementing load shifting programs. The obtained results reveal that the proposed model could reduce the peak load by 6.05 MW and enhance the system flexibility level by 88 %.

Peer‐to‐peer electricity trading via a bi‐level optimization approach for renewable energy‐driven microgrids connected to the distribution grid

AbstractThis study employs a sophisticated bi‐level optimization methodology to model the most efficient operation of microgrids (MGs) within the operational framework of distribution companies (DCs). In this bi‐level optimization problem, the upper level strives to maximize the profits of both MGs owners and DCs, while the lower level is dedicated to ensuring load balance, managing distributed generation, and implementing load curtailment strategies. The coordination of power transmission is facilitated by the DCs. At the upper level of decision‐making, the optimal pricing strategies for power transactions are determined, accounting for various factors such as market prices, demand response programs, and uncertainties in wind speed. Through the utilization of a bi‐level optimization framework, this study comprehensively captures the complex interactions between MGs and DCs, taking into consideration the objectives and constraints of both entities. This approach offers a more precise representation of the decision‐making process in retail electricity markets, thereby providing valuable insights into the optimal operation of MGs within the DCs setting.

Robust economic-resilient operational planning of distribution networks considering multi-microgrids black-start service

The increasing growth of natural disasters and cyber-physical attacks has made the development of resilience enhancement strategies for distribution networks a significant challenge for operators. Hence, in this paper, a black-start strategy based on multi-microgrids capability is developed as a two-layer preventive-corrective measure within a cost-effective energy management framework. The preventive layer includes regulating bilateral contracts between the active distribution network operator and the multi-microgrids owner pre-event. The corrective layer includes islanding microgrids during the event, calling the responsive loads, and economic-resilient operational planning of the active distribution network post-event. In this regard, a hybrid robust multi-objective optimization approach is applied to model the proposed black-start strategy as a risk-averse portfolio. The effectiveness of the developed model is tested on the modified IEEE 33-bus distribution network. Results such as an 83.38 % load recovery of the active distribution network, a 65.78 % reduction in distribution system operator costs, and a 131.28 % increase in multi-microgrid owner profit clearly illustrate the enhanced resilience of the active distribution network within an economically robust framework, which indicates the effectiveness of the proposed strategy.

Techno-economic modeling of a preventive-corrective strategy based on black-start services to resilience enhancement of distribution networks

High-impact with low-probability (HILP) events such as natural disasters or cyber-physical attacks severely affect power systems. The distribution networks experience significant challenges during HILP events. So, strategies need to be developed to enhance distribution networks' resilience. Hence, in this paper, a resilience enhancement strategy based on the potential of multi-microgrids is developed. The proposed strategy has a two-layered structure consisting of preventive and corrective measures within the hierarchical centralized energy management framework. The preventive measures are based on the black-start bilateral contract between the active distribution network operator and the multi-microgrids owner. The corrective measures are based on islanding microgrids during the event. The LP-Metric-based multi-objective optimization approach is employed for economic-resilient operational planning of the active distribution network. Moreover, a scenario-based stochastic approach is utilized to deal with renewable resources and load uncertainties. On the modified IEEE 33-bus active distribution network, the effectiveness of the proposed strategy is evaluated using the supplied load as a resilience index. The proposed black-start strategy has resulted in an increase of 181.57 % in the supplied load of the distribution network, while a reduction of 83.12 % in its curtailed load has been achieved. Consequently, the resilience index increased by 84 %. Additionally, the distribution system operation cost was reduced by 87.44 %, while the multi-microgrid owner's profit increased by 1004.68 %. The obtained results demonstrate that distribution system resilience has been enhanced within an economic framework, which validates the effectiveness of the proposed strategy.

Smart vehicle-to-grid integration strategy for enhancing distribution system performance and electric vehicle profitability

This study presents a novel Vehicle-to-Grid (V2G) integration strategy. By utilizing the energy stored in electric vehicles (EVs) to inject power into the grid optimally during peak-load/high-tariff periods. This V2G strategy has two main objectives: enhancement of the system performance and offering financial profit to EV owners. The V2G strategy optimizes the energy exchange between EVs and the grid based on load leveling methodology through intelligent scheduling of charging/discharging times to alleviate grid stress during peak demand, leading to enhanced system performance. Also, it enables EV owners to sell their excess energy to the utility at favorable prices, which not only helps EV owners offset their charging costs but also turns their EVs into profitable assets. The contribution over recent similar studies is targeting multiple objectives simultaneously, besides employing a comprehensive set of factors, involving all relevant participants, and rigorous load modelling. The effectiveness of this strategy is demonstrated through comprehensive simulations with two different charging scenarios applied to two networks, RBTS-bus2 and ShC-D8. The results show the enhancement of system performance, as evidenced by improvements in voltage profiles and load curves, besides reductions in losses, operational costs, and feeder and transformer loading. Furthermore, the proposed approach maximizes the profit of EV owners.

A multi-objective two-stage stochastic programming approach for wind farms and demand side provider decisions using the weighted metric method

The rapid expansion of wind-based renewable energy sources highlights the critical need to enhance reserve capacity within power systems. This research aims to develop an effective model for harnessing the combined potential of flexible demand-side resources, acting as demand-side providers (DSPs), to address the inherent unpredictability of wind power. The DSP framework comprises three demand response aggregators: the capacity market program (CAP), energy storage systems (ESSs), and plug-in electric vehicle parking lots (PEVs PLs). To achieve this, a multi-objective two-stage stochastic programming model is introduced. It not only minimizes operational costs for system operators but also maximizes returns for demand-side resource owners supervised by DSPs in the energy and reserve market environment. The problem is tackled using a mixed-integer linear programming (MILP) approach employing a weighted metric methodology. Within this framework, the Weibull probability distribution function (WPDF) is utilized to model wind power uncertainty, while the truncated Gaussian distribution function (GDF) encapsulates the uncertainty regarding electric vehicle owner behavior. The results demonstrate that employing this method for simultaneous programming of energy and reserve not only reduces system operator costs but also enhances profits for resource owners.

Optimal Dispatch Strategy for Electric Vehicles in V2G Applications

The overutilization of electric vehicles (EVs) has the potential to result in significant challenges regarding the reliability, contingency, and standby capabilities of traditional power systems. The utilization of renewable energy distributed generator (REDG) presents a potential solution to address these issues. By incorporating REDG, the reliance of EV charging power on conventional energy sources can be diminished, resulting in significant reductions in transmission losses and enhanced capacity within the traditional power system. The effective management of the REDG necessitates intelligent coordination between the available generation capacity of the REDG and the charging and discharging power of EVs. Furthermore, the utilization of EVs as a means of energy storage is facilitated through the integration of vehicle-to-grid (V2G) technology. Despite the importance of the V2G technology for EV owners and electric utility, it still has a slow progress due to the distrust of the revenue model that can encourage the EV owners and the electric utility as well to participate in V2G programs. This study presents a new wear model that aims to precisely assess the wear cost of EV batteries, resulting from their involvement in V2G activities. The proposed model seeks to provide EV owners with a precise understanding of the potential revenue they might obtain from participating in V2G programs, hence encouraging their active engagement in such initiatives. Various EV battery wear models are employed and compared. Additionally, this study introduces a novel method for optimal charging scheduling, which aims to effectively manage the charging and discharging patterns of EVs by utilizing a day-ahead pricing technique. This study presents a novel approach, namely, the gradual reduction of swarm size with the grey wolf optimization (GRSS-GWO) algorithm, for determining the optimal hourly charging/discharging power with short convergence time and the highest accuracy based on maximizing the profit of EV owners.

Commodification and Exploitation of Children in Television Content Programs and New Media Content (Case Study of Fajar Labatjo Alias Fajar Sadboy)

Cases of commodification of children are increasingly common, various television programs and online media have shown many children who are considered viral. The television and online industries take advantage of the child's virality value to be converted into exchange rates. Commodification, which is a term that refers to the practice of converting use value into exchange value, has been used in this context. One of the things that has recently become the object of commodification by television and online media is children. Children are considered attractive to advertisers because they are relatively malleable and influenced by their surroundings, and can be used as an attraction for audiences because of their innocence. This study aimed to explore forms of commodification of children, especially in the viral context of Farar Labadjo or who is known to the public as Fajar Sadboy. To understand this form of commodification, this research adopted the basic theory of media political economy proposed by Vincent Mosco. The research method used in this research was critical discourse analysis developed by Norman Firclough. A qualitative approach with descriptive research type was used in this research. The results of this study revealed various forms of commodification of children in television showed and online media based on the theory of media political economy. In this context, children were exploited and forced to perform various activities in order to fulfill the demand for content in the program. Besides that, Children are also used as a tool to increase advertising rates and get a share of the profits for program owners. One of the most striking forms of commodification is the large number of similar programs that invite children because there is an exchange value from the large number of viewers and increased content traffic. This study concludes that there are various forms of commodification in Television and Online Media programs effectively "commodifying" children's virility and even taking advantage of controversies and tragic incidents for profit.

Financial Analysis of Small Fisherman's Hand Line Fishing Business in Kupang City

The development of the capture fisheries business requires every company and fishery business actor, especially small scale fishermen, to compete and increase the catch from each of their business activities. So that the level of business feasibility is a criterion for investment or long-term investment in a particular production. The purpose of this study was to analyze the handline busines with a vessel size of 2 GT -10 GT in the waters of the city of Kupang. The research method used is a survey method, determining respondents based on purposive sampling. Data collecting done by interview and observation. The data analysis used is descriptive qualitative and quantitative analysis. 30 fishermen who owned handline boats with a size of 2-10 GT were used as the samples. The research was conducted in March - May 2023 at the Tenau Beach Fishing Port (PPP), Kupang city. The business analysis carried out is to look at the average investment costs, fixed costs, operational costs, income and profits of fishing business within 1 year. The results showed that the average investment cost was IDR 161,361,619/year, the total cost was IDR. 354,999,991, -/year total revenue of IDR. 891.066.667.-/year and the ship owner's profit was IDR. 536,066,676,-/year. Based on the results of the analysis, the R/C Ratio and B/C Ratio values are greater than ≥ 1, so it can be concluded that the 2-10 GT small handline fishing vessel business in the waters of Kupang city is profitable and feasible to develop.

Charging station layout planning for electric vehicles based on power system flexibility requirements

Under the ambitious commitment of reaching carbon neutrality by 2060, China promotes both the deployment of renewable energy and the development of electric vehicles. Renewable fluctuations in the supply side and EV charging burden in the demand side propose higher requirements for the system flexibility. Optimized station layout and charging schedule could coordinate the load curve, provide system flexibility and accommodate the variable renewables. However, previous work only focuses on maximizing the profit of station holders and vehicle owners, lacking the consideration of the broader impact on power system. Here, we propose an EV charging station layout optimization methodology considering not only the EV charging behavior, sequential charging demand, but also its further impact on power system. The station layout and charging schedule are co-optimized with an integrated power system model. Applying the proposed methodology to Jiangxi in 2025, a cumulative charging station capacity of 1412, 1092, and 1415 MW is recommended in workplaces, residences, and shopping centers, respectively. Increased renewable energy integration and decreased thermal power generation are realized, achieving reduced carbon emissions of 800 kilotons. This work provides an effective methodology for slow charging station layout from the perspective of the integrated power system.

Influence of technological innovation characteristics on the survival period of SMEs in the service industry: Evidence from Korea

This study analyzes the effects of innovation and innovation characteristics on the survival of Small and Medium-sized Enterprises (SMEs) in the service industry. After analyzing around 22,300 innovative SMEs in Korea's service industry using Kaplan-Meier analysis, it is confirmed that the better the overall technological prowess (T-grade), the longer the survival period until delinquency (overdue payments for more than three months) and default. The technological innovation characteristics that significantly affect the survival period are derived using a time-dependent Cox model. Owner capability, productization capability, and profit prospects are found to positively affect the survival period of excellent SMEs, while R&D capability, technology superiority, and market status are found to have a negative effect. From an optimization perspective, if R&D capability is above an appropriate level, it can hinder the survival period of SMEs. Thus, it is interpreted that the more positive the market status, the more negative the survival period, because it accelerates the inflow of new competitors. Considering that owner capability, productization capability, and profit prospects are appraisal items based on business feasibility along with the technological prowess of SMEs, it is concluded that technology with business feasibility has a significantly positive effect on the survival period of innovative SMEs in the service industry.

Dynamic scheduling of market price-based combined heat–power-constrained renewable microgrid

Abstract In this paper, a market price-based combined heat–power dynamic dispatch model for a microgrid is presented. The microgrid comprises cogeneration units and wind and solar power-generation units. A battery and a heat storage tank are incorporated to optimally balance variations in heat-and-power load demands. The proposed model explores the impact of market prices of electricity, heat supply and load variability on the optimal schedule such that profit maximizes and emission, loss and waste heat are minimized. The Weibull probability distribution function is applied to characterize the uncertain renewable power variable in the model and to find the over- and under-scheduling costs. The problem is solved using an improved differential evolution algorithm in which a fuzzy membership module is appended to obtain a solution having the highest attainment for the selected multiple objectives. The results show that the proposed model can handle uncertain heat–power demand and price scenarios to produce feasible and optimal schedules with owner profits, heat utilization and renewable share varying between 10.55–115.97%, 72.51–90.39% and 26.82–38.05%, respectively.

A proactive strategy for virtual power plants including multiple private owners equipped with energy storages

This paper presents a novel framework for the optimal operation strategy of a virtual power plant (VPP) comprises a set of various stakeholders such as renewable energy sources, distributed energy resources with private owners, and flexible loads in day-ahead and real-time energy markets. In order to model the strategic behavior of private owners within the VPP geographic location, a proactive stochastic bi-level model is developed, where the upper level assigns to minimize expected operation cost of the technical VPP and the lower level associates with maximizing the private owner's profits. Note that, the uncertainty set encompasses the real-time energy market price as well as renewables power production. Afterward, the proposed bi-level model is converted to a mixed-integer linear programming problem through Karush–Kuhn–Tucker optimality conditions along with strong duality theory. Moreover, the impacts of implementation of three different pricing schemes including fix pricing, time of use, and real-time pricing on the market trading strategy of VPP and also its interactions with private owners is investigated through comparative case studies. The obtained results demonstrate that the proposed approach could allow both VPP aggregator and private owners to take the advantages of the market conditions.

Strategic Pricing of Electric Vehicle Charging Service Providers in Coupled Power-Transportation Networks

Electric vehicles (EVs) are developing at a rapid pace. The EV charging station (EVCS) plays an indispensable role in supplying EV charging demand and promoting the transportation electrification. This work proposes a pricing method to maximize the profit of EVCS owners, charging service providers. First, the tri-level pricing framework is established where EVCSs set charging prices at the upper level, EVs make route and charging options at the middle level, and the electricity price is cleared in power distribution network at the lower level. Second, the singlelevel optimal pricing model is established with traffic flow assigned, power generation scheduled and electricity price explicitly contained via KKT conditions. The proposed model considers the impact of EVCS configuration on traffic assignment and pricing strategy. The operation risk brought by uncertainties is managed via Conditional value at risk. Third, a decomposition solution algorithm is proposed, which tackles the computational burdens and overcomes the infeasibility of existing algorithms. Finally, the real-world urban network-based case studies verify the effectiveness and merits of the proposed method.

Financial Analysis of Sea Grape Cultivation (Caulerpa sp) Basic Plugging System Quadrant Net Method in Southeast Maluku Regency

Sea grape cultivation (Caulerpa sp) is a fishery business activity that can increase the income of coastal communities; therefore, it is necessary to project profit and business opportunities in the future on an ongoing basis based on the profitability. This study aims to analyze the financial feasibility of sea grape (Caulerpa sp) cultivation with basic plugging system quadrant net method. This study used survey research methods, the type of data needed is primary and secondary data, data collection used a purposive sampling technique, and data collection used a triangulation method. Data analysis consists of total production costs, revenue, profit, break-even point and R/C ratio. The results of the financial analysis of the sea grape cultivation business (Caulerpa sp) using the quadrant net step-by-step system method show that the total production cost is IDR 3,368,333/year, business revenue is IDR 12,600,000/year, the business owner's profit is IDR 9,231,667/year, R/C ratio is 3.74, BEP (unit) is 21 kg and BEP (sales) is IDR 317,106. Sea grape cultivation (Caulerpa sp) with basic plugging system quadrant net method is financially feasible to be developed in order to increase in the people income in the coastal areas of Southeast Maluku Regency.

Dynamic UAV Deployment for Differentiated Services: A Multi-Agent Imitation Learning Based Approach

Unmanned Aerial Vehicles (UAVs) have been utilized to serve on-ground users with various services, e.g., computing, communication and caching, due to their mobility and flexibility. The main focus of many recent studies on UAVs is to deploy a set of homogeneous UAVs with identical capabilities controlled by one UAV owner/company to provide services. However, little attention has been paid to the issue of how to enable different UAV owners to provide services with differentiated service capabilities in a shared area. To address this issue, we propose a multi-agent imitation learning enabled UAV deployment approach to maximize both profits of UAV owners and utilities of on-ground users. Specially, a Markov game is formulated among UAV owners and we prove that a Nash equilibrium exists based on the full knowledge of the system. For online scheduling with incomplete information, we design agent policies by imitating the behaviors of corresponding experts. A novel neural network model, integrating convolutional neural networks, generative adversarial networks and a gradient-based policy, can be trained and executed in a fully decentralized manner with a guaranteed <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -Nash equilibrium. Performance results show that our algorithm has significant superiority in terms of average profits, utilities and execution time compared with other representative algorithms.

A Comprehensive Review of the Bidirectional Converter Topologies for the Vehicle-to-Grid System

Over the past decade, there has been a great interest in the changeover from cars powered by gasoline to electric vehicles, both within the automotive industry and among customers. The electric vehicle–grid (V2G) technology is a noteworthy innovation that enables the battery of an electric vehicle during idling conditions or parked can function as an energy source that can store or release energy whenever required. This results in energy exchange between the grid and EV batteries. This article reviews various bidirectional converter topologies used in the V2G system. Additionally, it can reduce the cost of charging for electric utilities, thus increasing profits for EV owners. Normally electric grid and the battery of an electric vehicle can be connected through power electronic converters, especially a bidirectional converter, which allows power to flow in both directions. The majority of research work is carried out over the converters for V2G applications and concerns utilizing two conversion stages, such as the AC-DC conversion stage used for correcting the power factor and the DC-DC conversion stage for matching the terminal voltage. Furthermore, a bidirectional conversion can be made for an active power transfer between grid–vehicle (G2V) and V2G effectively. This review explores and examines several topologies of bidirectional converters which make it possible for active power flow between the grid and the vehicle and vice versa. Moreover, different types of charging and discharging systems, such as integrated/non-integrated and on/off board, etc., which have been used for electric vehicle applications, are also discussed. A comparison study is carried out based on several other factors that have been suggested. The utilization of semiconductors in power converters and non-conventional resources in charging and discharging applications are the two improving technologies for electric vehicles.

NILAI TAMBAH DAN KEUNTUNGAN PENGOLAHAN BUNGA KRISAN PADA BERKAT ANUGERAH FLORIST DI KELURAHAN KAKASKASEN I, TOMOHON UTARA (Added Value And Profit Of Chrysanthemum Flower Processing At Berkat Anugerah Florist In Kakaskasen I Village, Tomohon Utara)

Chrysanthemum flower is a type of ornamental flower plant cultivated in Indonesia which has good prospects for development and becomes a source of income that generates added value due to the inclusion of primary product processing elements into new products. The objective of this research is to analyze the benefits and added value of chrysanthemum flowers at the Berkat Anugerah Florist in Kakaskasen I Village, North Tomohon Sub District, Tomohon City. The benefit of this research is that it can provide a source of information about the added value and benefits of chrysanthemum. Methods of data collection, namely primary and secondary data as well as the variables measured in this study for a series of hanging round cranes, standard standing cranes, large standing cranes and table flowers. The results of the data obtained will then be processed and analyzed in more depth using added value analysis according to the hayami method. &#x0D; The research result showed that has been done showed that flower arrangements at the Florist which provide added value as follows for large standing bouquets have the greatest added value with an added value of Rp. 32,907.08, two hanging round cranes with an added value of Rp. 14,017.21, the three table flowers with an added value of Rp. 7,602.22 and finally a standard standing crane with an added value of Rp. 7,377.78. Meanwhile, the biggest profit for business owners is a large standing crane with a profit of Rp. 32407.08. Then the hanging crane with a profit of Rp. 13017.21. Standard standing faucet with a profit of Rp. 6,477.77. Table interest with the smallest profit of Rp. 6.102,22.

Robust bidding strategy of battery energy storage system (BESS) in joint active and reactive power of day-ahead and real-time markets

The most important applications of an Energy Storage System (ESS) in power systems are energy arbitrage along with procurement of Ancillary Services (ASs). In addition to economic benefits, ESS also improves network reliability and stability. In this paper, a bidding strategy model of a Battery Energy Storage System (BESS) in a Joint Active and Reactive Power Market (JARPM) in the Day-Ahead-Market (DAM) and the Real-Time-Market (RTM) using a robust framework is presented. In this study, the BESS model is considered a price-taker, with the private owner trying to maximize its profit while facing price uncertainty. In the first stage of the proposed model, JARPM is cleared in a deterministic way without the presence of BESS to determine hourly prices of active and reactive power. In the second stage, by determining the prices uncertainty interval and the robust budget for DAM and RTM, the robust bidding model aims to maximize the BESS owner's profit, using the robust counterpart formation of the objective function and dual theory. The proposed robust formulation based on a Mixed Integer Non-Linear Programming Model (MINLP) is implemented in the GAMS software environment on the IEEE-24-RTS test system. The result shows that by the participation of BEES in the market, the average price of active and reactive power is reduced by 2 % & 3 % respectively. In addition, the overall cost of DAM execution is reduced markedly. On the other hand, the proposed framework in three case studies also guarantees a suitable profit level for the private owners of BESS (30–35 $/hour).