Traditional Distribution System Research Articles

Coupling geospatial suitability simulation and life cycle carbon emissions towards uncertain optimization planning for wind-photovoltaic-hydrogen multi-energy complementary system

Building clean and modern multi-energy complementary systems is a direct path to decarbonization in energy sector. The introduction of hydrogen energy with high operational flexibility enables the multi-energy complementary system to fully accommodate renewable energy. The key issues of the optimization planning of the wind-photovoltaic-hydrogen multi-energy complementary system that need to be solved are how to finely characterize the carbon reduction potential of the system, consider the uncertainty of supply and demand in the modeling process, and incorporate the energy supply business of electric vehicles into the planning. This study constructs optimization planning model with the following three progressive stages: geospatial suitability simulation based on geographic information system, uncertainty analysis based on probabilistic scene generation technology, and capacity configuration optimization. Unlike previous studies, this model measures and optimizes the carbon emissions level of the system from a lifecycle perspective. The empirical study results indicate that carbon emissions optimization from the perspective of the entire life cycle would directly affect the system configuration results. From a lifecycle perspective, the carbon emissions accounting result is about 9 times higher than that obtained only during the operation phase. Compared to the traditional distribution system, the carbon emissions of the system constructed in this study decreased by 82.75 %. In addition, considering that the system provides charging services for electric vehicles, which can reduce carbon emissions by about 93.6 % but only increase costs by about 29.4 %. The computational results justify the proposed optimization framework. This study provides optimization models for the system, and the implementation framework can support technical reference for practice.

Multi-overlapping disaster rolling recovery of unbalanced distribution systems collaborated with repair crews and mobile power sources

Traditional distribution system recovery strategies can handle non-overlapping disasters, typically a single N-k failure, rather than multi-overlapping disasters. Multi-overlapping disaster refers to a scenario in which a system experiences multiple N-k failures, with a new N-k failure occurring before the system has been fully restored from the previous one. To address the recovery problem under multi-overlapping disasters, a rolling recovery model for unbalanced distribution systems that considers both repair crews (RCs) and mobile power sources (MPSs) is proposed. The proposed rolling recovery model can automatically optimize preceding recovery strategies based on the grid topology and the state of each resilient resource at the overlapping moment of each disaster. Case studies are conducted on the modified IEEE 33-node test system to demonstrate the concept of multi-overlapping disaster recovery. Compared to traditional methods that treat multi-overlapping disasters as multiple individual disasters, the case studies demonstrate that the proposed model can reduce load shedding by about 6.91 %, which verifies the effectiveness of the proposed methodology for updating recovery strategies at overlapping moments of disasters.

Potential Use of Fryze’s Approach-Based Power Theories in Waveform Distortion Contribution Assessment

This paper explores the potential of some remarkable power theories derived from Fryze’s approach to assessing waveform distortion contributions. The analysis is limited to a simplified system composed of one load and one voltage source connected by a point of common coupling. Finally, some keys to assess are proposed for two types of systems: strong networks (traditional distribution systems) and weak networks (islanded microgrids).

A Critical Study of the Structural Change of the Capitalist Socio-Economic Structure by Combining Marxism and Modern Information Technology

Abstract Modern information technology networks have changed people’s production and lifestyle, leading to new changes in the economic structure of capitalist society and bringing new challenges to the use of Marxism to criticize capitalism. Based on the knowledge of Marxist economics, this paper provides a critical overview of the changes in the economic structure of capitalism from two aspects: the changes in the forms of capital exploitation and the expansion of capital exploitation relations. Using the complex network theory in modern information technology, a capitalist socio-economic model is constructed to perform a critical quantitative analysis of the structural changes in the capitalist socio-economic structure. A case study of capitalist socio-economic structure is conducted in conjunction with the previous model. The results show that the entropy of capitalist wealth E is decreasing in the case that the parameters of N = 1000, m = 6, and M = 40 are determined, and the traditional labor distribution system and production mode will be eliminated by history in the end. Through a critique of capitalist economic mechanisms, it deepens people’s knowledge of capitalist economic structure.

Modelling of Prosumer Behaviour in Peer-to-Peer Energy Trading Environment Through Markov Chain Analysis

The emergence of distributed generation has become significant over the last few years, bringing several challenges to traditional grid-connected electricity distribution systems. Technical limitations of integrating these new renewable distributed generation to the existing electricity grid made new avenues to change the way of energy consumption, which converted traditional consumers to prosumers. Peer-to-peer energy trading platforms enable these prosumers to trade electricity with their neighbours in microgrids where new electricity markets emerge with new technologies. This research introduces a concept of peer-to-peer energy trading for grid-connected electricity distribution networks. In its initial phase, the study models prosumer load sharing for households equipped with solar PV and battery energy storage. This modelling employs a Markov chain, where energy share states are determined by parameters in a Markov transition probability matrix. These parameters are varied during different time intervals of the day, reflecting varying cost components under a Time-of-Use (TOU) tariff structure. This approach enables prosumers to meet their load demand cost-effectively, fostering engagement in peer-to-peer energy exchanges. The proposed Markov model is simulated with 15-minute spot pricing intervals, demonstrating effective management of prosumer load demands in the peer-to-peer market environment, showcasing the potential benefits of this innovative energy trading approach.

A Review of Demand-Side Resources in Active Distribution Systems: Communication Protocols, Smart Metering, Control, Automation, and Optimization

Power systems have been going through a barrage of transformations due to the recent developments in the field, such as deregulation and restructuring of the electric power supply chain, the proliferation of distributed generation (DG), and advancements in information and communications technologies. These have significantly impacted the approach to the planning, design, and operation of active distribution networks or systems. Due to this constant change, the system has become more complex to plan, maintain, and control. In this paper, the benefits and challenges of active distribution systems relative to traditional passive and active distribution systems are evaluated and investigated while the management and operational characteristics of demand-side resources in active distribution systems (ADS) are studied. In a typical ADS, there exist several vulnerabilities and threats that eventually pose a challenge in the control and automation of substations. These vulnerabilities and threats are reviewed, and potential mitigation measures are suggested. Also in this paper, the communication technologies and their implementation in terms of control and automation capabilities in active distribution networks are also studied. From this work, it is concluded that communication technologies play an integral role in the realization of more active distribution networks and that the Internet of Energy (IoE) is a major player in ADS in the reduction of faults due to human error, fast responses, and improving the stability of power supply. Cyber threats are also and will still be a continuous challenge in smart metering technologies and in substation automation systems (SAS), which will require frequent evaluation and mitigation measures so as not to prevent the power supply system from collapsing.

Enabling residential heating decarbonization through hydronic low-temperature thermal distribution using forced-air assistive devices

Space heating represents approximately one-tenth of the United States’ energy use and has a breadth of potential for emission reduction. An element of space heating, hydronic heat distribution methods, use water supply temperatures up to 82.2 °C (180 °F). However, this operating temperature can be incompatible with high-efficiency heat generation systems, which typically provide heating temperatures of up to 60 °C (140 °F). In this work, a low-cost retrofit solution is developed using experimentally validated computational tools by incorporating an airflow distributor that preferentially directs the airflow from a fan to enhance heat transfer over the finned-tube heat exchanger found in conventional baseboards. The same heat output of traditional baseboard heat distribution systems operating at higher water supply temperatures (71.1 – 82.2 °C) can also be achieved at lower temperatures (≤ 60 °C). Specifically, results indicate that the technology can produce more than a 46.7% improvement in the heat transfer output at temperatures as low as 60 °C (140 °F), effectively matching the same output range achieved by 71.1 – 82.2 °C (160–180 °F) water supply temperatures. An important benefit of this solution is that it utilizes the existing infrastructure as opposed to requiring the replacement of the entire distribution system. Additionally, this technology enables existing building infrastructure to be coupled with newer, high-efficiency heating systems such as condensing boilers, solar-thermal systems, and geothermal/air-to-water heat pumps that may produce lower water supply temperatures (60 °C). A geospatially resolved techno-economic and environmental analysis is completed and presented to further understand the equivalent carbon footprint of enabling higher efficiency heat generation systems with the improved efficiency heat distribution system disclosed. Using 2021 grid-average emission factors, an emission reduction of up to a 67.5% decrease (2658 kgCO2/yr) for a single-family home, depending on state and climate region, could be realized by replacing a traditional natural gas-fired boiler with an air-to-water heat pump coupled to the same high-temperature heat distribution system along with the low-cost retrofit solution. A complete CO2 emission reduction of 6688 kgCO2/yr, depending on state and climate region, could be realized if all electricity is further renewably sourced. Thus, this study provides a possible pathway towards enabling the reduction of operational emissions in space heating.

The Impact of AI and Information Technologies on Islamic Charity (Zakat): Modern Solutions for Efficient Distribution

This article examines the impact of artificial intelligence (AI) and information technology solutions on the distribution of Islamic charitable giving, specifically Zakat. The study identifies challenges faced by traditional Zakat distribution systems, such as inefficient resource allocation, inadequate transparency and accountability, and difficulty in identifying eligible recipients. Through an analysis of AI-driven solutions and block-chain based platforms, the article proposes that these technologies can address these challenges by automating and optimizing distribution processes, enhancing transparency and tracking, and improving the identification and verification of eligible recipients. The discussion section evaluates the effectiveness and feasibility of AI and information technology solutions for Zakat distribution, compares the proposed solutions with existing approaches, and outlines potential challenges in implementation. It also provides recommendations for further research, development, and policy implementation to ensure the responsible and effective use of these technologies in the context of Islamic charitable giving. The conclusion highlights the key findings and their implications for the future of Zakat distribution. It calls for the adoption of AI and information technology solutions to enhance the efficiency and effectiveness of Islamic charitable giving, ultimately benefiting millions of people in need across the globe.

Distribution Automation: Enhancing Efficiency and Reliability in Power Distribution Systems

This paper investigates the importance of distribution automation in power distribution systems. The introduction highlights the challenges faced by traditional distribution systems, such as high losses, inefficient processes, and outdated infrastructure. Distribution automation, referred to as smart grid technology, is a transformative solution that integrates advanced technologies and automation devices to enhance power distribution, operational efficiency, and system reliability. This paper provides a comprehensive examination of various distribution automation devices, such as remote fault indicators, smart relays, automated switches and reclosers, automated capacitors, voltage regulators and load tap changers, feeder monitors, and transformer monitors. The importance of distribution automation is emphasized, including enhanced reliability, improved operational efficiency, enhanced customer satisfaction, and the integration of renewable energy resources. Opportunities for distribution automation, such as enhanced reliability, improved operational efficiency, enhanced data collection and analysis, integration of distributed energy resources, and demand response programs, are highlighted. Furthermore, the challenges associated with distribution automation, such as the cost of implementation, technical complexity, cybersecurity risks, workforce training and transition, and regulatory and policy framework, are discussed. Addressing these challenges requires collaboration among utilities, regulators, policymakers, and technology providers. The successful implementation of distribution automation can revolutionize power distribution, leading to more efficient, reliable, and sustainable electricity supply.

An Optimal Selection and Placement of Distributed Energy Resources Using Hybrid Genetic Local Binary Knowledge Optimization

In recent times, the virtual power plant (VPP) is gaining more attention in power system engineering due to its tremendous potential in enhancing sustainable urbanism, in which, it supplies clean energy from distributed generators. Electricity is deemed a basic requirement for future automotive and ultra-modern technologies. The deficiency of traditional energy resources and their complex generation process make the production cost of electricity increase dramatically. Moreover, traditional power distribution systems are encountering issues in distributing electrical energy to fulfill customer demands. Therefore, this paper proposes a novel power management system named ‘the hybrid genetic local binary knowledge (HGLBK) algorithm’ to manage power distribution in the transmission lines and to optimize the total operation cost of the network. The hybrid optimization algorithm effectively controls the load by supplying the surplus power load to the adjacent feeders thereby optimally selecting and placing the distributed energy resource (DER). The proposed concept is implemented at Kayathar, Tamil Nadu in India, and their real-time data are utilized for modeling the VPP. The proposed VPP concept is implemented in the IEEE-9 bus system and the performance of VPP is simulated using the MATLAB software. The performance of the proposed HGLBK algorithm is assessed by comparing its effectiveness with the existing approaches.

Supply–demand scenario generation of active distribution systems using conditional generative adversarial networks

The intermittency of distributed renewable energy resources (DRERs) and the fluctuation of load demand make the supply–demand scenario of active distribution systems significantly different from that of traditional radial distribution systems. Scenario generation is an effective tool for describing supply–demand uncertainty and is therefore of great significance for the planning and operation of active distribution systems with a high penetration of DRERs. This paper proposes a supply–demand scenario generation method for active distribution systems using conditional generation adversarial networks (CGANs). The proposed method is data-driven and utilizes neural networks to learn and represent distribution laws from data without revealing the expression probability distribution. Specifically, it first extracts the features information of historical scenarios as the conditional control vectors (CCVs), then embeds the CCVs to the input of the generator network and discriminator network to achieve conditional control. The proposed approach is verified by the actual time series wind power data and load profiles. Results show that the proposed method can efficiently generate supply–demand scenarios satisfying various statistical characteristics and capture complex spatial–temporal correlations.

Coordinated Repair Crew Dispatch Problem for Cyber–Physical Distribution System

Reasonable repair crew dispatch can accelerate the service restoration in distribution systems after some components are damaged. It is worthy noting that as traditional distribution systems are involving into cyber-physical distribution systems composed of physical distribution networks and cyber networks, damaged components in these two networks should be coordinately repaired to achieve maximal restoration performance. However, repair crews in physical distribution networks are scheduled without considering the impact of the corresponding cyber networks in existing repair crew dispatch strategies. In this paper, a coordinated model is proposed to co-optimize the repair crew schedule of the two networks. Besides the cyber-physical interdependence between the two networks, backup power systems, topology reconfiguration and intentional distribution generator (DG) island are also modelled to improve the repair performance. The proposed model is formulated as a mixed-integer linear programming model, and its effectiveness is verified on the modified IEEE 33-node and 123-node test systems. Numerical results highlight the necessity of co-optimizing repair crew dispatch in both physical distribution networks and cyber networks.

Incorporating P2P Trading Into DSO's Decision-Making: A DSO-Prosumers Cooperated Scheduling Framework for Transactive Distribution System

The emerging prosumers have changed the way traditional distribution systems operate. While harvesting the benefits of Distributed Energy Resources (DERs), prosumers’ self-dispatching and internal energy trading can also bring technical challenges and economic loss to the distribution system operator (DSO). To this end, a DSO-prosumers cooperated scheduling framework for the transactive distribution system, which considers the impact of prosumers’ peer-to-peer (P2P) energy trading, is proposed in this paper. First, by incorporating the P2P energy trading among prosumers into scheduling, prosumers and the DSO can cooperate to optimize the power flow. Meanwhile, the Nash bargaining theory is introduced to achieve the optimal allocation of interests. Then, to improve the computational efficiency, the established bargaining problem is decomposed into two subproblems, i.e., the optimal physical power flow problem, and the virtual power flow bargaining problem. Finally, in order to protect individual privacy, each problem is solved via the alternating direction method of multipliers (ADMM) algorithm in a distributed manner. Case study based on the IEEE 33-bus test system verifies the effectiveness of the proposed method.

Reclaiming traditional food systems in alternative food networks. Insights from Mexico city peri-urban agriculture

ABSTRACT In the literature on how to make food systems just and sustainable, we often look at Alternative Food Networks (AFN) as a way forward. AFNs are initiatives that pose alternatives to conventional food systems (driven by productivism and harmful to human and environmental health). Despite this broad understanding of what AFNs can be, in practice, most cases presented in the literature look surprisingly alike. In this article, we challenge the scholars working on alternative food networks to look beyond the narrow definition of AFNs by considering cases of traditional agriculture and distribution systems. We illustrate this argument using the case of chinampas, a traditional agricultural system in Mexico City that contributes in different ways to local sustainability. Analysing semi-structured interviews conducted on site, we argue that chinampas can be defined as an alternative food network because of their modes of production, their diverse distribution channels, and their economic practices. We conclude that traditional food systems present an alternative to conventional food systems and should therefore be included in AFN research. Broadening the definition of AFNs so to include traditional and non-institutional practices can help identify diverse cases fitting this new lens, including in the Global North; it can also contribute to affirming the value of these traditional systems in building sustainable and just societies.

A Modeling Toolkit for Comparing AC and DC Electrical Distribution Efficiency in Buildings

Recently, there has been considerable research interest in the potential for DC distribution systems in buildings instead of the traditional AC distribution systems. Due to the need for performing power conversions between DC and AC electricity, DC distribution may provide electrical efficiency advantages in some systems. To support comparative evaluations of AC-only, DC-only, and hybrid AC/DC distribution systems in buildings, a new modeling toolkit called the Building Electrical Efficiency Analysis Model (BEEAM) was developed and is described in this paper. To account for harmonics in currents or voltages arising from nonlinear devices, the toolkit implements harmonic power flow, along with nonlinear device behavioral descriptions derived from empirical measurements. This paper describes the framework, network equations, device representations, and an implementation of the toolkit in an open source software package, including a component library and graphical interface for creating circuits. Simulations of electrical behavior and device and system efficiencies using the toolkit are compared with experimental measurements of a small office environment in a variety of operating and load configurations. A detailed analysis of uncertainty estimation is also provided. Key findings were that a comparison of predicted versus measured efficiencies and power losses in the validation testbed using the initial toolkit implementation predicted device- and system-level efficiencies with reasonably good accuracy under both balanced and unbalanced AC scenarios. An uncertainty analysis also revealed that the maximum estimated error for system efficiency across all scenarios was 3%, and measured and modeled system efficiency agreed within the experimental uncertainty in approximately half of the scenarios. Based on the correspondence between simulation and measurement, the toolkit is proposed by the authors as a potentially useful tool for comparing efficiency in AC, DC, and hybrid AC/DC distribution systems in buildings.

Optimal placement of locally controlled voltage regulators in traditional distribution systems using the IMDE algorithm

Abstract One of the main goals of the designers of the power systems is the reduction of extra systems costs. The main part of these costs is related to energy loss, and the methods for loss reduction significantly lead to a cost reduction. The voltage regulators (VRs) can be used along the distribution feeders and the improvement of the network voltage profile results in the loss reduction of the system. However, the important problem is the installation location of this equipment to have the most profitability with the lowest cost. In this paper, the optimal placement of the VRs along the distribution systems is performed using intersect mutation differential evolution algorithm. The method aims to reduce loss and the primary investment and maintenance of the VRs. To this aim, the studies are performed on two scenarios for two standard distribution systems with 33 and 69 buses. A genetic algorithm (GA) is also utilized for the VRs placement to validate the results. The results show that the VRs reduce the loss to about 15% of the maximum system load. Moreover, the improved differential evolution algorithm performs better than the GA and makes the objective function in the minimum value.

Research on the Intelligent Distribution System of College Dormitory Based on the Decision Tree Classification Algorithm

The trend toward designing an intelligent distribution system based on students’ individual differences and individual needs has taken precedence in view of the traditional dormitory distribution system, which neglects the students’ personality traits, causes dormitory disputes, and affects the students’ quality of life and academic quality. This paper collects freshmen's data according to college students’ personal preferences, conducts a classification comparison, uses the decision tree classification algorithm based on the information gain principle as the core algorithm of dormitory allocation, determines the description rules of students’ personal preferences and decision tree classification preferences, completes the conceptual design of the database of entity relations and data dictionaries, meets students’ personality classification requirements for the dormitory, and lays the foundation for the intelligent dormitory allocation system.

Characteristics, Problems and Prospects of the Development of Barrage Film

As an organic part of online films and TV series, barrage is characterized by instant interactions, originality, and content sharing, conducive to easing the boredom of watching films in cinemas, reducing the burden caused by the traditional film distribution system, and promoting the recreation of audiences. However, there are many problems faced with the development of the barrage film such as disorder of emotions, misinterpretation of texts, cultural interruption, and lack of innovation. It should be related to the audience, the system of the industry, the cultural characteristics, and traditions of the sector. Therefore, in order to promote the development of the barrage film, major breakthroughs can be made through three aspects, including the choice of movies, the overview of experience, and the innovation. Besides, it is necessary to focus on the technical advantages of traditional cinemas, promote the subjective consciousness of audiences to integrate into the film, and encourage the creation of original and high-quality films, optimizing the structure of China’s film industry and promoting its continuous prosperity.

Consumer Embeddedness and Motivations for Farmers Market Patronage: A Qualitative Exploration in Minnesota, USA

The rise in popularity of farmers markets in the United States reflects consumers’ negative response to more traditional food distribution systems. Farmers markets provide consumers with a more local and often more personal food purchasing experience. The purpose of this study was to examine consumer motivations to patronize farmers markets through the lens of social, spatial, and natural embeddedness. A qualitative approach was employed utilizing semi-structured, in-depth interviews. These interviews were conducted in person using a set of predetermined questions and revealed nine themes. The findings indicate that two types of consumer choices with different properties exist in farmers market patronage (e.g., the choice of a particular farmers market vs. the choice of a particular vendor at the market). Inconsistency occurs in consumer choice patterns (e.g., economic saving does not greatly affect the choice to shop at a particular farmers market but can determine whom to buy from once at the market), implying that situational dynamics play a critical role at the point of purchase. While this study supports the usefulness of embeddedness as a conceptual framework for understanding farmers’ market patronage, it demonstrates a distinction between motivation to patronize the market and shopping behaviors exhibited once there.

Cybersecurity Enhancement of Smart Grid: Attacks, Methods, and Prospects

Smart grid is an emerging system providing many benefits in digitizing the traditional power distribution systems. However, the added benefits of digitization and the use of the Internet of Things (IoT) technologies in smart grids also poses threats to its reliable continuous operation due to cyberattacks. Cyber–physical smart grid systems must be secured against increasing security threats and attacks. The most widely studied attacks in smart grids are false data injection attacks (FDIA), denial of service, distributed denial of service (DDoS), and spoofing attacks. These cyberattacks can jeopardize the smooth operation of a smart grid and result in considerable economic losses, equipment damages, and malicious control. This paper focuses on providing an extensive survey on defense mechanisms that can be used to detect these types of cyberattacks and mitigate the associated risks. The future research directions are also provided in the paper for efficient detection and prevention of such cyberattacks.