Distribution Network Investment Research Articles

Collaborative operation strategy of multiple microenergy grids considering demand‐side energy‐sharing behavior

AbstractIn recent years, as the energy market continues to change, an emerging business model is gradually emerging as an energy provider (EP). The participation of EP as an independent entity in the distribution network investment model not only helps realize a win–win situation for multiple stakeholders but also promotes the innovation and sustainable development of the energy industry. However, in the energy system, EPs, grids, microenergy grids (MEGs), and shared energy storage service providers usually belong to different stakeholders, and their respective pursuit of profit maximization can easily lead to uncontrolled competition, which greatly reduces market efficiency. To address the varying ownership structures within the energy system, a refined bilevel coordinated optimization operation model is introduced, rooted in the master–slave game theory. In this model, the EP assumes the role of the master, while the MEG operator and the shared energy storage operator (SESO) act as slaves. A peer‐to‐peer energy‐sharing mechanism between MEGs and an energy trading mechanism between MEGs and shared energy storage is used to further increase the local consumption rate of new energy. Simulation results demonstrate that the proposed method not only boosts the revenues of an EP, SESO, and MEG operators but also effectively accounts for the MEG's energy storage needs and SESOs' profitability.

Deep Transfer Learning Based Surrogate Modeling for Optimal Investment Decision of Distribution Networks

At present, the distribution network investment is faced with complicated operation strategies and various new investment elements. It's difficult to evaluate the impact of different investment measures on the final benefits due to the complicated physical modeling and potential coupling correlation, which contributes to that the traditional model-driven decision-making method may not work. This paper proposes a novel deep transfer learning based surrogate-assisted method to address the challenge. The key idea is to develop a surrogate model to replace the input-output correlation between the certain investment measure and its benefits, which can be further reformulated into mixed-integer linear constraints and embedded into the classical investment decision-making model for the target distribution network. As the model is trained through a designed two-stage adaptive transfer learning algorithm, which can extract the similar rules from other distribution networks, the dilemma of insufficient effective samples is alleviated, while a neural network constrained algorithm is designed to linear the surrogate model for simplifying the optimization. Numerical cases prove the effectiveness of the surrogate model on correlation mining and show the superiority of the surrogate-assisted method in achieving the precision investment for target distribution network.

Multiscale dynamic time‐domain simulation of regional integrated energy considering seasonal load difference

AbstractTo improve the multiscale dynamic dispatching and distribution capacity of regional integrated energy, a multiscale dynamic time‐domain dispatching model of regional integrated energy with seasonal load difference is proposed. The master–slave game coordination planning model of regional comprehensive energy multiscale dispatching is constructed, and the difference fusion model of regional comprehensive energy multiscale dynamic dispatching is established by using the active and reactive power coordination control method according to the influence factors of voltage deviation and line overload absorption. Maximize the weight factors of the positive contribution of distributed photovoltaic (PV) power to the active distribution network. On the basis of the quantitative regulation analysis method of demand response power based on the equivalent slope of the load curve, establish an energy absorption capacity analysis model of seasonal load differentiation. On the basis of the dynamic interaction between different systems, a multiscale dynamic time‐domain analysis and dynamic simulation model of regional integrated energy is constructed by using the method of PV absorptive capacity assessment on both sides of demand uncertainty, to achieve multiscale dynamic time‐domain feature extraction of integrated energy and integrated scheduling of seasonal load differences. The simulation results show that the multiscale dynamic time‐domain allocation of regional comprehensive energy using this method improves the energy dispatching and balanced allocation capability, and the time‐domain dynamic allocation capability is good. Considering the terminal load demand and the electricity price demand formulated by the distribution network investment operators, the seasonal load response and comprehensive dispatching capability are improved.

Multi-objective Collaborative Optimization Method for Intelligent Distribution Network Operation and Maintenance

Most of the conventional multi-objective coordination methods for active distribution network operation are designed based on the principle of weighted random algorithm, which has limited application scope and cannot simplify the algorithm and reduce the complexity of the solution process, resulting in poor stability of active distribution network lines and potential faults. Based on this, this algorithm is introduced to carry out multi-objective collaborative optimization for intelligent distribution network operation. Firstly, the node types for the intelligent distribution network are defined and the power flow of the distribution network is analyzed. Secondly, a multi-objective configuration model for the lower level of the operation is constructed to maximize the distribution network investment and operation benefit index. A multi-objective collaborative optimization algorithm based on an enhanced particle swarm optimization algorithm is designed to reduce the operation cost of the distribution network and enhance the security and stability of the distribution network system during operation. The simulation test results show that after the application of the new method, the intelligent distribution network voltage stability index Vm is always less than 1, and the line stability has been significantly improved.

Designing electricity distribution networks: The impact of demand coincidence

With the global effort to reduce carbon emissions, clean technologies such as electric vehicles and heat pumps are increasingly introduced into electricity distribution networks. These technologies considerably increase electricity flows and can lead to more coincident electricity demand. In this paper, we analyze how such increases in demand coincidence impact future distribution network investments. For this purpose, we develop a novel model for designing electricity distribution networks, called the distribution network reconfiguration problem with line-specific demand coincidence (DNRP-LSDC). Our analysis is two-fold: (1) We apply our model to a large sample of real-world networks from a Swiss distribution network operator. We find that a high demand coincidence due to, for example, a large-scale uptake of electric vehicles, requires a substantial amount of new network line construction and increases average network cost by 84% in comparison to the status quo. (2) We use a set of synthetic networks to isolate the effect of specific network characteristics. Here, we show that high coincidence has a more detrimental effect on large networks and on networks with low geographic consumer densities, as present in, e.g., rural areas. We also show that expansion measures are robust to variations in the cost parameters. Our results demonstrate the necessity of designing policies and operational protocols that reduce demand coincidence. Moreover, our findings show that operators of distribution networks must consider the demand coincidence of new electricity uses and adapt investment budgets accordingly. Here, our solution algorithms for the DNRP-LSDC problem can support operators of distribution networks in strategic and operational network design tasks.

Techno-economic model for long-term revenue prediction in distribution grids incorporating distributed energy resources

Distributed energy resources (DER) is a prevalent technology in distribution grids. However, it poses challenges for distribution network operators to make optimal decisions, estimate total investment returns, and forecast future grid operation performance to achieve investment development objectives. Conventional methods mostly rely on current data to conduct a static analysis of distribution network investment, and fail to account for the impact of dynamic variations in relevant factors on a long-term scale on distribution network operation and investment revenue. Therefore, this paper proposes a techno-economic approach to distribution networks considering distributed generation. First, the analysis method of the relationship between each investment subject and distribution network benefit is established by using the system dynamics model, and the indicator system for distribution network investment benefit analysis is constructed. Next, the distribution network operation technology model based on the dist flow approach is employed. This model takes into account various network constraints and facilitates the comprehensive analysis of distribution network operation under dynamic changes in multiple factors. Consequently, the technical index parameters are updated to reflect these changes. This updated information is then integrated into the system dynamics model to establish an interactive simulation of the techno-economic model. Through rigorous verification using practical examples, the proposed method is able to obtain the multiple benefits of different investment strategies and be able to select the better solution. This can provide reference value for future power grid planning.

Multiyear stochastic wind generation investment planning with demand response in distribution system using improved water evaporation optimization

ABSTRACT This article proposes a multiyear distributed generation (DG) investment planning model with the coordination of demand response for making the decision of investment under uncertainty. The aim is to reduce the net present value related to the energy purchasing cost from the grid, investment cost including operation, and maintenance cost, emission penalty cost and energy losses cost. In this article, two investment planning models are developed with a total planning horizon of 20 years: (i) static investment planning model and (ii) dynamic investment planning model. In the static investment, DG is installed in first year, whereas for dynamic investment model, time period of DG installation is considered flexible. The impact of real-time demand response is also analyzed for both cases. The optimal distribution network investment planning problem, which includes multiple DG and demand response, is a complicated combinatorial optimization problem that traditional optimization methods are unable to tackle efficiently. Therefore, an improved water evaporation optimization algorithm is proposed and compared with the genetic algorithm, basic water evaporation algorithm, and particle swarm optimizationin this article. Both DG investment models are applied to the standard 33-bus radial distribution network.

An Optimization Approach for Distribution Network Planning Strategy Based on Combined Improved Cloud Model and Evidence Theory

With the continuous promotion of China’s power reform, higher requirements and standards have been put forward for distribution network investment decisions. However, the current work of distribution network project optimization in China has the problems of high subjectivity of investment decisions, fuzzy evaluation method, and high uncertainty of project optimization. In response to the above problems, this paper proposes a distribution network planning scheme optimization method based on the improved cloud model and evidence theory. Firstly, the optimal index system of the planning scheme is constructed from the dimensions of load transfer capacity, grid structure level, load capacity, investment benefit, social benefit, and life-cycle cost. Secondly, for subjectivity and ambiguity problems, the traditional degree of the membership function is replaced by a cloud model optimized by cloud entropy. Thirdly, the problem of uncertainty is addressed through evidence theory, while the game theory approach is used to identify conflicting evidence for evidence revision and fusion in the case of conflicting evidence arising from traditional evidence theory. Moreover, by improving the TOPSIS method and comparing the distances between the basic probability assignments of each planning scheme and the positive and negative virtual optimal solutions, the final decision results can be determined. Finally, the effectiveness and superiority of the proposed method are verified by experiments.

Research on collaborative planning model of main distribution network frame structure and distributed power generation

The electric power industry is going through a relatively large transformation period of energy development, and new electric power equipment such as distributed power supply has emerged as the times require. However, there is often a problem of insufficient coordination between the grid frame and distributed power in the main distribution network. This paper starts with the two main stakeholders, namely, the distributed generation operator and the distribution system operator, to build a two-level collaborative planning model for the main distribution network. With the help of particle swarm optimization algorithm and genetic algorithm with improved constraint factors, the model is solved. In the optimization process, two different scenarios are set, including and not including the distributed generation output, for comparative analysis, the comprehensive cost of the collaborative planning model with distributed generation output is less than that of the collaborative planning model with traditional generation output. The results show that the model in this paper can not only optimize the overall line, but also reduce the excessive investment of the main distribution network, which is more in line with the future development of my country’s power industry, can improve the profits of related enterprises, and achieve a win-win effect.

Optimal allocation of cloud energy storage system in low-voltage distribution network

In low-voltage distribution networks, an uncontrolled charging of plug-in hybrid electric vehicles (PHEVs) brings about intensive peak loads in distribution transformers’ daily load curves. Due to insulation degradations, this issue ends in transformers’ loss of life (LOL) and early aging. The notion of cloud energy storage system (CESS) with larger power and energy capacities enables consumers to have access to cheaper energy storage facilities. Thanks to CESS installation, semi-smart, controlled, and low-cost charging of PHEVs could be realized to relieve the transformer’s peak loads and reduce the peak-to-average (PAR) ratio of load curve. To do so, this paper develops an optimal planning model for siting and sizing of a CESS to accommodate the charge/discharge requirements of consumers who are willing to collaborate with CESS and hence, implementing a controlled charging of PHEVs. The proposed model considers the investment and operation costs of both CESS and distribution network. It also models the transformer LOL costs. The PHEVs, in private garages, are supposed to be connected to the grid through a smart module whose connection time and status is optimally scheduled and controlled through the operator of the CESS. The CESS charging/discharging is regulated such that the previously determined time targets of PHEV owners are satisfied. To assess performance of the proposed model, extensive numerical studies are carried out in terms of techno-economic indices say as PAR reduction, conducting energy arbitrage, power losses reduction, and etc. Results are discussed in depth.

A novel investment strategy for renewable-dominated power distribution networks

Aiming at the problem of insufficient adaptability to the new elements of the new power system in the current distribution network investment method, this paper innovatively proposes a distribution network investment method based on the new power system. By constructing a source-grid-load-storage-side investment calculation model, the investment in the new power system can be accurately calculated. First, the distributed power investment is calculated from the two aspects of new construction and renovation. Secondly, construct the grid investment demand and grid investment capacity measurement model, and obtain the grid side investment model by weighted summation. Then, a model for calculating the scale of investment that can be saved due to demand-side response is constructed, and the cost of demand response is subtracted to obtain a model for calculating the scale of investment that can be saved on the load side. Finally, the energy storage side investment calculation model is constructed from the power supply side, grid side, user-side energy storage investment, and energy storage investment benefit. The research results are applied to the empirical area, and scientific guidance is provided to realize the precise investment in the area.

Nexus Tax Laws and Economies of Density in E‐Commerce: A Study of Amazon's Fulfillment Center Network

We quantify the distortionary effects of nexus tax laws on Amazon's distribution network investments between 1999 and 2018. We highlight the role of two features of the expansion of Amazon's network: densification of the network of distribution facilities and vertical integration into package sortation. Densification results in a reduction in the cost of shipping orders, but comes at the expense of higher facility operating costs in more expensive areas and lower scale economies of processing shipments. Nexus laws furthermore generate additional sales tax liabilities as the network grows. Combining data on household spending across online and offline retailers with detailed data on Amazon's distribution network, we quantify these trade‐offs through a static model of demand and a dynamic model of investment. Our results suggest that Amazon's expansion led to significant shipping cost savings and facilitated the realization of aggregate economies of scale. We find that abolishing nexus tax laws in favor of a non‐discriminatory tax policy would induce the company to decentralize its network, lowering its shipping costs. Non‐discriminatory taxation would also entail lower revenue, however, as tax‐inclusive prices would rise, resulting in a fall in profit overall. This drop and the decline in consumer welfare from higher taxes together fall short of the increases in tax revenue and rival profit, suggesting that the abolishment of nexus laws would lead to an increase in total welfare.

The Economics of Demand-side Flexibility in Distribution Grids

To avoid unnecessary distribution network investments, distribution tariffs are expected to become more cost-reflective, and DSOs are expected to procure flexibility. This will provide an implicit and an explicit incentive to provide demand-side flexibility. In this paper, we develop a long-term bi-level equilibrium model. In the upper level, the DSO optimizes social welfare by deciding the level of investment in the distribution network and/or curtailing consumers. The regulated DSO also sets a network tariff to recover the network and flexibility costs. In the lower level, the consumers, active and passive, maximize their own welfare. We find that implicit and explicit incentives for demand-side flexibility are complementary regulatory tools, but there are limits. If network tariffs are too imperfect, the resulting consumption profiles can become too expensive to fix with curtailment. We also find that it is difficult to set an appropriate level of compensation because of the reaction by prosumers.

Optimization Model of Electric Vehicles Charging and Discharging Strategy Considering the Safe Operation of Distribution Network

Against the background of carbon neutrality, the power dispatching operation mode has undergone great changes. It not only gradually realizes the coordinated control of source–grid–load–storage, but also strives to realize the multi-level coordination of the transmission network, distribution network and microgrid. Disorderly charging and discharging of large-scale electric vehicles (EVs) will have a great negative impact on the distribution network, but aggregating EVs and guiding them to charge and discharge in an orderly manner will play a positive role in delaying investment in the distribution network. Therefore, it is urgent to adopt an effective scheduling control strategy for electric vehicle charging and discharging. First, a variety of indexes were set to analyze the influence of EVs access on distribution network and the correlation between the indexes. Then, by defining the EVs penetration rate and the load simultaneous rate, the charging load planning of EVs was calculated. Based on the simultaneous load rate, the regional electricity load plan was calculated, and a configuration model of distribution capacity suitable for charging loads in different regions was constructed. Finally, an optimal dispatch model for electric vehicles considering the safety of distribution network was proposed and the distribution transformer capacity allocation model was used as the optimization target constraint. Compared with most optimized dispatch models used to maximize aggregator revenues and reduce peak-to-valley differences and load fluctuations in distribution networks, this model could effectively reduce unnecessary investment while meeting regional distribution transformer needs and maintaining distribution network security. Taking the improved IEEE 34-bus systems as an example, the simulation analysis was carried out and the investment demand of distribution network under the condition of disordered and orderly charge and discharge was compared. The results show that the proposed optimal scheduling method can effectively reduce the load fluctuation of distribution network, keep the voltage offset within the allowable voltage deviation range, and can effectively delay the investment of distribution network.

Research on Improving the Precision Investment Level of Distribution Network Based on the Whole Process Control Means of Big Data

With the continuous and stable growth of China’s national economy, the demand of the whole society for the total power supply is higher and higher, which requires the power enterprises to continuously improve the power supply. However, if the total power supply is increased, the power enterprises need to invest a lot of power transmission facilities, which requires a huge investment in the distribution network. Therefore, different economic development levels among regions will lead to the difference of distribution network investment, which is also an important content of distribution network investment decision. Therefore, we must control the whole process through big data, which will provide a better evaluation system of distribution network investment benefits. Through the whole process management and control mode, the State Grid can build a systematic and complete distribution network investment decision-making system, which will improve the accuracy of distribution network infrastructure project investment. By analyzing the income ratio, the power grid can get scientific economic investment analysis report. Through big data technology, the power grid can realize a variety of data of distribution network operation, such as maintenance plan optimization, distribution network operation process optimization, project investment income, equipment health status analysis, etc., which can provide auxiliary decision support. Through the three aspects of technical benefit, social benefit and economic benefit, we can evaluate the operation efficiency of equipment, which can construct the index system of project investment benefit. Firstly, this paper constructs the whole process management and control system of distribution network based on big data. Then, this paper constructs the accurate investment index system of distribution network. Finally, some suggestions are put forward.

Cost reduction for energy loss and capacitor investment in radial distribution networks applying novel algorithms

Cost reduction for energy loss and capacitor investment in radial distribution networks applying novel algorithms

Optimal Configuration Method for EV Charging Station in Distribution Network Considering User Adjustment under V2G mode

The existing charging piles planning method of distribution network does not consider the influence of user participation in regulation. Hence, an optimal planning method of electric vehicle charging piles in V2G mode is proposed, considering users’ influence of regulation. An EV charging pile planning model is established to minimize the investment and operation cost of distribution network and charging station, and the genetic algorithm with elitist retention strategy is used to solve the model. The examples show the effectiveness of the proposed method.

Optimization Decision Method based on Nonlinear Analytic Hierarchy Process for Active Distribution Network Planning

Aiming at the investment environment of the distribution network, this paper deeply analyses the investment conditions of the distribution network, introduces the analytic hierarchy process and modern investment theory, establishes the analytic hierarchy of the investment conditions in the distribution network project, and proposes an optimization decision-making method of investment plan in distribution network. This paper also establishes two new concepts: condition score and correlation score, uses the improving analytic hierarchy process to calculate the weights, proposes a priority ranking method for distribution network projects, and gives detailed process. to formulate distribution network investment plans. The application of the proposed method to optimize the distribution network planning scheme shows that the optimization investment scheme given has better investment benefits under the condition of the power supply reliability requirements of the distribution network and investment capital constraints.

The Challenges of Estimated Billing on Electricity Consumers in Nigeria: A Review

Despite Government efforts, over 4.6 million Nigerians are on the estimated billing system. Nigeria has one of the highest aggregate technical, commercial and collection losses at 43.65%, which has been attributed to low investments in distribution networks exacerbated by the slow progress of metering of customers which has posed a protracted liquidity challenge to the industry. There is a need to reduce these losses; hence this paper examined the Nigerian electricity billing system through meta-analysis of literature, with a focus on estimated billing, the metering situation, and its challenges in Nigeria. Finally, solutions such as the implementation of a cost reflective tariff scheme are proffered through recommendations to distribution companies and the Federal Government of Nigeria.

Research on distribution network investment allocation method considering actual demand and investment benefit

under the new situation, improving the quality and efficiency has become an important means to enhance the core competitiveness of power grid enterprises and ensure the steady improvement of business efficiency. Distribution network is an important part of the power grid of power grid enterprises, and the current distribution network investment distribution lacks scientific quantitative basis. Therefore, how to scientifically plan the scale of power grid investment and improve the efficiency and efficiency of power grid investment is the foundation to ensure the safe and stable, efficient and economic operation of power grid, and it is also a practical problem to be solved.