Customer Interruption Costs Research Articles

Measuring the economic and societal value of reliability/resilience investments: case studies of islanded communities

ABSTRACT Large-scale disasters have exposed vulnerabilities in energy systems and interdependent infrastructure, underscoring the importance of proactively mitigating risks to critical infrastructure. This paper focuses on strengthening power system planning by incorporating the costs associated with prolonged and extensive power interruptions to bolster resilience. To achieve this, duration-dependent customer damage functions are developed, capturing the direct, indirect, and societal impacts of power interruptions, ranging from short, localized events to widespread and long-duration ones. The research methodology is applied to three islanded communities facing substantial resilience challenges with limited data availability. Three customer interruption cost surveys are conducted with local electricity customers, yielding valuable insights into duration-dependent customer damage functions for direct, indirect, and monetizable societal costs. Significantly, the power interruption cost estimates derived from these functions vary considerably from those in the contiguous U.S. and even between the different islanded communities, reflecting their distinct hazard profiles and geographical characteristics. In conclusion, this study demonstrates the potential of duration-dependent customer damage functions to enhance power system resilience. It also identifies several areas for further research, paving the way for a more robust and resilient power infrastructure.

Reliability-Oriented Planning Framework for Smart Cities: From Interconnected Micro Energy Hubs to Macro Energy Hub Scale

Energy hubs, as the prospect of future energy systems, are efficient and reliable frameworks for the generation, conversion, storage, and consumption of energy in multicarrier energy systems. Existing urban facilities in smart cities (SCs) can be considered as micro energy hubs (MIEHs), whereas the SC itself can be regarded as a macro energy hub incorporating its MIEHs. Moreover, the interconnection of MIEHs is particularly important to achieve high-reliability and economical smart city energy systems (SCESs). This article presents a framework for reliability-oriented expansion planning of multicarrier energy systems in SCESs. The proposed framework tends to improve energy not supplied and customer interruption cost indices for both heat and electrical loads, as well as the energy index of reliability. The proposed multicarrier energy system facilitates interoperability between different energy carrier systems to supply electricity and heat demand simultaneously. The interconnection of MIEHs is modeled through linearized alternating current optimal power flow and natural gas network constraints, whereas full interaction of the electricity and gas networks is realized by the power-to-gas systems and combined heat and power systems. The effectiveness of the proposed mixed-integer linear programming planning model is demonstrated in the Dättwil district (Switzerland) through four scenarios, analyzing the effect of MIEH interconnection, as well as the gas and electricity interaction within an MIEH. Simulation results confirm the effectiveness of the proposed method due to a significant reduction in total investment cost and a considerable improvement in reliability indices.

A new hybrid CBSA-GA optimization method and MRMI-LSTM forecasting algorithm for PV-ESS planning in distribution networks

Renewable energy sources have gained significant attention as a priority option for cost reduction and emissions reduction from fossil fuels. However, their utilization introduces uncertainty and decreases reliability in distribution networks. To effectively harness these resources and mitigate their uncertainties, this paper proposes a model for determining the optimal size and location of photovoltaic (PV) systems and energy storage systems (ESS) in electrical energy distribution networks. The objective is to minimize network power losses, voltage deviations, customer interruption cost, PV system installation cost, and ESS installation cost. To achieve this, the study employs a hybrid forecasting model based on MRMI feature selection and LSTM deep neural network to forecast daily solar power and electrical load for 12 different days across various months of the year, considering the desired electrical energy distribution system. Subsequently, these predicted values serve as 12 scenarios to determine the optimal location and size of PV systems and ESSs. The optimization problem is tackled using a novel hybrid optimization method known as the Coot Bird Search Algorithm-Genetic Algorithm (CBSA-GA). The efficiency of the proposed algorithm is evaluated by comparing its results with GA, PSO, and CBSA algorithms. Among the optimization methods discussed in this paper, CBSA-GA excels in reducing the objective function by accurately determining the capacities of PV resources and ESS. Compared to scenarios where distributed generation (DG) is not integrated into the distribution network, the implementation of CBSA-GA algorithm results in significant improvements in the objective function for Cases 1 to 3. The reductions achieved in the objective function are 9.10 %, 11.88 %, and 24.39 % for Cases 1, 2, and 3, respectively. These findings highlight the effectiveness of CBSA-GA in optimizing the integration of DG resources, specifically in terms of reducing the objective function and improving system performance.

An operationally induced approach to reliability-oriented ACOPF-constrained planning of interconnected multicarrier energy hubs: An MILP formulation

Energy hubs (EHs) are considered as prospective frameworks for future high-reliability energy systems, as interfaces for generation, conversion, storage, and consumption of different energy carriers. Interoperability and interconnection of the EHs may improve the reliability of the system, therefore, the system planning and operation should be constrained by AC optimal power flow (ACOPF), to ensure connectivity of the EHs and optimality of the operation. The main contribution of this paper lies in introducing an innovative operationally induced approach to reliability-oriented ACOPF-constrained planning of interconnected EHs. This manuscript presents an efficient mixed integer linear programming (MILP) formulation, aiming to result in optimal reliable and economic planning and scheduling of the microgrids including interconnected EHs, while reducing the computational effort. Further, this research investigates the effect of EHs interconnection and demand response (DR) on optimal planning and scheduling of microgrids. This is implemented on the IEEE 33-bus distribution test system by operating EHs in interconnected mode, revealing a significant improvement in reliability (CIC: customerinterruption cost, ENS: energy not supplied, and EIR: energy index of reliability), economic (DSOBenefit, EnergyRevenue, INVC &OPC: investment and operation costs), renewable energy share (REC: renewable energy curtailment, REUF: renewable energy utilization factor, and WPPCC: Wind-photovoltaic generation Pearson correlation coefficient), and microgrid-maingrid energy exchange.

Providing an optimal demand response program through placement of automatic switches and energy storage systems to improve the reliability of power distribution networks

AbstractCreating highly reliable distribution networks is becoming increasingly vital in today's society. In a similar vein, utilities have issues in properly planning and developing distribution networks, both to reduce their imposed costs and to meet the demands of their consumers. In the presence of Demand Response Program (DRP), this research provides a coordinated architecture that considers automated switches and Energy Storage Units (ESUs) placement with the uncertainty of repair time to enhance the reliability of power distribution network. The suggested objective for optimal placement of automatic switches, ESUs and DRPs is to minimize a combination of reliability index (SAIDI) and Total Cost of system (TC). Customer interruption cost, energy not supplied cost, automated switch investment, ESU investment, ESU participation cost, and DRP cost are all considered as parts of the total cost. The proposed method is used in four different cases on a common reliability test system, followed by numerous sensitivity studies, in order to illustrate applicability and efficiency of suggested method.

Optimal Phase Balancing in Electricity Distribution Feeders Using Mixed-Integer Linear Programming

A mixed-integer linear programming (MILP) model that includes reductions in neutral current, feeder energy-loss cost, customer interruption cost, and labor cost is developed to derive the optimal phase-swapping strategy to enhance the phase balancing of distribution feeders. The neutral current of the distribution feeder is reduced by the phase-swapping strategy so that the tripping of the low-energy overcurrent relay can be prevented and customer-service interruption costs and the labor cost to execute the phase-swapping works can be justified by the energy-loss reduction obtained. The novelty of the study is its derivation of the phase-swapping strategy using mixed-integer linear programming to solve the problem of the unbalance of the distribution feeders. A Taipower distribution feeder is used to derive the phase-swapping strategy to demonstrate the proposed MILP model for phase balancing. The comparison of the phase currents and neutral current before phase-swapping reveals that the three-phase balance was not only significantly improved, but that the voltage unbalance was also decreased dramatically using the proposed phase-swapping strategy.

A Risk-Based Framework to Improve a Distribution System’s Resilience against Earthquakes

Earthquakes and other natural disasters have caused significant damages to power systems, indicating the necessity to enhance power system resilience. This paper proposes a risk-based resilience enhancement framework against earthquakes that considers their stochastic nature. The proposed framework supports the decision-making of distribution system operators (DSOs) for retrofitting substation components and underground cables to enhance distribution system resilience. The framework consists of four phases, namely earthquake modeling, vulnerability assessment, risk assessment, and resilience enhancement. An attenuation relationship is used to model the earthquake characteristics. Vulnerability assessment includes the failure probability calculation of substation components and a fault-tree method application to examine the seismic vulnerability of the substation. Conditional value at risk (CVaR) is used to assess the seismic risk, and includes the estimation of repair cost and customer interruption cost due to the damage caused by the earthquake, as well as the power generation cost of the distributed energy resources (DERs) used to meet the demand locally. A modified risk reduction worth (RRW) metric is adopted to determine the optimal retrofitting strategy to enhance resilience. The proposed framework was applied to a real distribution substation to examine the effectiveness of substation component retrofitting for resilience enhancement.

Multi Microgrid Framework for Resilience Enhancement Considering Mobile Energy Storage Systems and Parking Lots

This paper proposes a practical and effective planning approach that takes advantage of the mobility and flexibility of mobile energy storage systems (MESSs) to increase distribution system resilience against complete area blackouts. MESSs will be very useful for boosting the system’s resilience in places affected by disasters when the transmission lines are damaged. A joint post-disaster restoration strategy for MESSs and PEV-PLs is proposed, along with distributed generation and network reconfigurations, to reduce total system costs, which include customer interruption costs, generation costs, and MESS operation and transportation costs. The integrated strategy accounts for the uncertainty of the production of wind- and solar-powered microgrids (MGs) and different forms of load demand. Therefore, this paper assesses the effect of MESSs on distribution system (DS) resilience in respect of MG cost reduction and flexibility. Due to the multiple networks, PEV-PLs, DGs, and MESS limitations, the suggested restoration problem is stated as a mixed-integer linear programming problem. The suggested framework is complemented using a benchmark testing system (i.e., 33-bus DS). To assess the effectiveness of the proposed model, the model’s output is contrasted with results from typical planning and a traditional model. The comparison of the data shows that the suggested model, in addition to MESSs, effectively achieves a large decrease in cost and enhances the DS resilience level.

Dynamic Economic Dispatching Considering Time-Coupling Spinning Reserve Response Risk with High Penetration of Wind Power

Aiming at the problem that the current dynamic economic dispatch (DED) fails to consider the response risk of spinning reserve caused by the fluctuation and uncertainty of wind power, we work out a DED problem considering time-coupling spinning reserve response risk while the stochasticity and variability arising from RESs are taken into consideration. The developed framwork unified the response risk of reserve caused by forced shutdown of the unit into the response risk caused by time coupling. The expected customer interruption cost (ECOST) and the expected abandoned wind cost considering this reserve response risk are added to the objective function. While seeking the minimum objective function, the system is automatically configured with suitable reserve to ensure the consistency of the system’s response risk in each period. An improved multi-universe parallel quantum genetic algorithm was used to solve the model. Numerical examples and analysis prove the effectiveness and feasibility of the proposed method.

A Stochastic Markov Model for Maintenance Scheduling in the Presence of Online Monitoring System

Active electricity distribution systems (AEDS) play an important role on the social welfare that is highly relies on the reliability of these systems. In this regard, maintenance management tries to decrease component failures via repairing component that are more possible to be failed in operation time. In this manuscript, a stochastic MINLP maintenance scheduling model is proposed in which the reliability of AEDS's equipment is considered through employing a three-state Markov model. This model helps us to embed the uncertainty of AEDS's equipment service restoration time by considering online monitoring system for it and FLISR functions in AEDS. In this paper, minimizing the total cost of AEDS is determined as objective function considering total customer interruption cost, total cost of energy not supplied, total cost of prevented maintenance actions, total corrective maintenance actions cost implemented in the AEDS and total cost of implementing online monitoring for AEDS's component in maintenance scheduling. The proposed framework is applied in two different cases, Case I is maintenance scheduling without online monitoring system and Case II is maintenance scheduling with online monitoring system. The aforementioned cases are implemented in an IEEE Test System as well as a real scale distribution network. Moreover, several sensitivity analyses are implemented for indicating the efficiency of the proposed model.

Optimal Preventive Maintenance Planning for Electric Power Distribution Systems Using Failure Rates and Game Theory

Current electric utilities must achieve reliability enhancement of considerable distribution feeders with an economical budget. Thus, optimal preventive maintenance planning is required to balance the benefits and costs of maintenance programs. In this research, the proposed method determines the time-varying failure rate of each feeder to evaluate the likelihood of future interruptions. Meanwhile, the consequences of feeder interruptions are estimated using interruption energy rates, customer-minutes of interruption, and total kVA of service areas. Then, the risk is assessed and later treated as an opportunity for mitigating the customer interruption costs by planned preventive maintenance tasks. Subsequently, cooperative game theory is exploited in the proposed method to locate a decent balance between the benefits of reliability enhancement and the costs required for preventive maintenance programs. The effectiveness of the proposed method is illustrated through case studies of large power distribution networks of 12 service regions, including 3558 medium-voltage distribution feeders. The preventive maintenance plans resulting from the proposed method present the best compromise of benefits and costs compared with the conventional approach that requires a pre-specified maintenance budget.

IOT-based social-economic management of distribution system with the high penetration of renewable energy sources

• A new internet of things frame work is developed for smart power grids with high data measure accuracy and low control latency. • A stochastic framework based on the point estimate approach (PEA) is developed which can handle the uncertainty effects through some limited concentration points. • Social-economic analysic of distribution systems with renewable sources. • Deploying IoT to monitor the system and minimize the cost. • Introducing the modified CSA for solving the problem in the stochastic environment. This paper investigates the social and economic aspects of the distribution system in the presence of different types of renewable energy sources. It suggests an internet-of-thing (IoT) based framework to monitor and measure the distribution system data with higher accuracy and controllability with very low latency. The proposed multi-objective framework considers the cost objective function as the economic index and the total customer interruption costs as the social index. A min-max fuzzy multi-objective framework is designed to optimize the objective functions using the modified clonal selection algorithm (CSA). In order to enhance the social-economic aspects of the distribution system, optimal reconfiguration is analyzed as a useful tool for the operator. A stochastic framework based on the point estimate approach (PEA) is developed which can handle the uncertainty effects through some limited concentration points. The appropriate performance of the proposed model is assessed on the IEEE 69-bus distribution system.

Power Dispatching of Transportable Energy Storage System for Post-disaster Restoration Scheme of Port: The AES-Based Joint Restoration Scheme

In recent years, the rapid development of artificial intelligence, Big Data, Cloud Computing, etc., rapidly develops, synergy development of the transportation-energy-information based triple play has been accelerated. The all-electric ship (AES) is satisfied with the demand for both the unreasonable resource configuration of port energy system and low energy efficiency caused by sudden disasters. As a power carrier unit, AES has great potential in improving the resistance of port power distribution network for large-area blackout. Therefore, in this paper, a joint post-disaster restoration scheme for AESs and distribution generation scheduling in ports’ microgrid (MG) is proposed to minimize the cost of post-disaster restoration including customer interruption cost and AES operation cost. The time-space network (TSN) model which describes the scheduling of AESs is applied to compare the grid resilience and flexibility of AES-based joint dispatching and only relying on distributed generation equipment in post-disaster restoration. The joint scheduling problem is formulated as a mixed-integer linear programming. The proposed model and scheme are tested in a system with 3 ports and 4 AESs verified the resilience of the port power distribution network with AESs.

Preparatory Operation of Automated Distribution Systems for Resilience Enhancement of Critical Loads

This paper proposes a stochastic optimization model for preparatory operation of distributed energy resources that enhance the resilience of critical loads against extreme weather events. Adopting hurricanes as a case study of extreme weather events, a high-precision spatio-temporal hurricane impact analysis model is first developed to enhance the situational awareness of automated distribution systems and generate a set of hurricane-induced outage scenarios. A stochastic mixed-integer conic programming model is then developed for co-optimizing the preparatory schedule of distributed energy storage and distributed generation ahead of hurricane along with post-event decisions of restoring services to customers, while minimizing the expected cost of energy not served as the index to measure the resilience. The proposed model prioritizes the service restoration of critical loads by integrating customer interruption cost function, which captures the customer type and outage costs as well as interruption duration in making restoration decisions. Additionally, a conic power flow model is adopted to accurately capture network operation constraints in pre- and post-outage scenarios. Numerical results conducted on the IEEE 33-bus system show that the proposed model can enhance the performance of automated distribution systems in responding to hurricane-induced outages by efficiently utilizing grid resources to improve the resilience of critical loads.

A hybrid method for recloser and sectionalizer placement in distribution networks considering protection coordination, fault type and equipment malfunction

An effective way to improve distribution system reliability is to place switches and protective equipment in the optimal location. Commonly, in the placement problem, the use of equipment in the designated location is assumed to be possible. But in practice, to establish protection coordination between the equipment, it is necessary to remove or relocate some of the equipment. This paper aims to increase distribution companies' profits and reduce customer interruption costs through a feasible solution. A new hybrid method for equipment placement has been proposed that simultaneously solves the protection coordination problem. While determining the optimal number and location of reclosers and sectionalizers, the proposed method ensures protection coordination between equipment (new and existing devices). Furthermore, to achieve a more realistic and accurate model, factors such as equipment malfunction, fault types (transient and permanent), and the relationship between these faults have been carefully considered to formulate the proposed method. In this method, to solve the optimization problems related to equipment placement and protection coordination, genetic algorithm, and linear programming in MATLAB software have been used, respectively. A real-life distribution network has been utilized to evaluate the proposed method, and the results show the capability and robustness of this method.

Switches optimal placement of automated distribution networks with probability customer interruption cost model: A case study

Sectionalizing switches (SSs) have been installed in Distribution networks with the aim of providing maneuver and ring points, so raising service reliability. It is usually assumed that these switches are totally reliable although, in practice, they have not ideal efficiency at all times. In fact, the switches might occasionally face failure, which decreases their capability with the aim of enhancing system reliability. A model with the aim of considering the failure probability of the switches in their optimal placement issue is proposed in this paper. The model aids with the aim of minimizing the whole costs of SSs and the interruption costs incurred through interrupted users. This study uses the discrete Markov chain model with the aim of obtaining the malfunction possibility under various states. Afterwards, the placement problem is solved with the aim of obtaining the global optimal solution with genetic algorithm. The advantage of the suggested model has been investigated according to sensitivity analysis and various scenarios. The research illustrates how the outcomes of the placement issue have been influenced through the uncertainty arising from the possible failure of the switches and how ignoring the uncertainty is able to cause deviation of the anticipated profit from the real one. The efficiency of the suggested procedure has been evaluated and shown through investigating on the Ahwaz city distribution network. The outcomes of simulation prove the ability and precision of the suggested procedure.

Reliability assessment by sensitivity analysis due to electrical power sequential tripping for energy sustainability

A drastic change has occurred over the last few years in the electrical power business, where a significant worldwide revolution has taken place and changing the design of electrical powers. It has become imperative for the utility to handle the electrical power reliably as poor performance can lead to electrical power outages which will inevitably have an adverse impact on a nation’s economy and society. Hence, the power company should consider devising suitable pre-emptive measures to avoid the possibility of electricity supply disruptions. These disruptions may have a potentially catastrophic effect on the running of the system, which may even include consumers having to bear monetary costs. This research looks at the valuations of risk and reliability cost/worth-based customer interruption cost due to static system sequential tripping. The performance of the suggested techniques are evaluated through comparing risk-based valuations and reliability cost/worth valuations corresponding to the transfer capability results which are correlated to the static system sequential tripping. The results have demonstrated that the unpredictable sequential tripping must not be ignored from the transmission reliability margin determination. Hence, the proposed techniques are likely to play an important role for energy sustainability of the future.

V2G Strategy for Improvement of Distribution Network Reliability Considering Time Space Network of EVs

Reliability is an important index which determines the power service and quality provided to customers. As the demand increases continuously and the system changes in accordance with the environmental regulation, the reliability assessment in the distribution system becomes crucial. In this paper, we propose methods for improving the reliability of the distribution system using electric vehicles (EVs) in the system. In this paper, EVs are used as power supplying devices, such as a transportable energy storage system (ESS) which supplies power when fault occurs in the system, and by using a time–space network (TSN) in particular, EV capacity in accordance with the load arrival time was calculated. Unlike other existing reliability assessments, we did not use the average load of customers. Instead, by taking into account the load pattern by times, we considered the priority for load supply in accordance with the failure scenarios and failure times. Based on the priority calculated for each time of failure and failure scenario, plans for EV operation to minimize expected customer interruption cost (ECOST), the reliability index in the distribution system, were established. Finally, a case study was performed using the IEEE RBTS (Roy Billinton Test System) 2 Bus and the performance of the model proposed in this paper was verified based on the result.

Self‐healing enhancement through co‐deployment of automatic switches and electric vehicle PLs in an electricity distribution network

Accomplishing highly reliable distribution grids is more and more important to today's modern society. Along the same line, self-healing is one of the most distinguishing features of the smart grid that could improve the reliability of the system up to a logical extent. In this study, the reliability of the electrical distribution grid has been improved by optimal allocation of automatic switches and parking lots (PLs). Thus, the optimal places of switches and PLs are determined with the purposes of minimising a combined effect of the customer-based (system average interruption duration index) and cost-based [total reliability cost (TRC)] reliability indices. The TRC includes customer interruption costs, PLs and automatic switches investment costs, and the total cost of PLs incorporation in the service restoration process. The proposed approach is implemented in three different planning scenarios: switch placement, PL placement, and joint switch and PL placement. The particle swarm optimisation method is employed to solve optimisation problems. Finally, a standard reliability test system bus number four of the Roy Billinton test system (RBTS 4) is used to demonstrate the efficacy of the proposed method.

Optimal Switch Placement in Distribution Systems: A High-Accuracy MILP Formulation

A new solution method is introduced to the problem of optimally deploying manual and automatic switches in distribution systems, where the product of two continuous variables and the inverse of a continuous variable are reformulated as a linear relation. This leads to a (mixed integer linear problem) MILP power flow formulation too. The objective function includes cost and reliability. The cost term itself includes capital investment, installation, and maintenance costs (MC) as well as customer interruption cost (CIC); while the reliability term is represented by system average interruption duration index (SAIDI). The problem is formulated as a MILP, which guarantees a global optimal solution. The effectiveness of the proposed method is validated through various case studies and sensitivity analyses on the RBTS4, followed by a comprehensive discussion and analysis of results. The proposed MILP formulation prescribes fewer switches while achieving lower SAIDI, compared to that of a previous MINLP formulation.