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In response to growing reliance on electricity and gas systems, this paper introduces a stochastic bi‐level model for the optimized integration of these systems. This integration is achieved through sizing and allocating of power‐to‐gas (P2G) and gas‐to‐power (G2P) units. The first level of the model focuses on decisions related to P2G and G2P unit installations, while the second level addresses optimal system operation considering decisions made from first level and stochastic scenarios. The primary aim is to enhance energy‐sharing capabilities through coupling devices and mitigate wind generation curtailment. An economic evaluation assesses the model's effectiveness in reducing costs. N − 1 contingency analysis gauges the integrated system's ability to supply load under emergency conditions. Two new indices, performance of the electricity system and performance of the natural gas system, are proposed for N − 1 contingency analysis. These indices quantify the proportion of the supplied load to the total load, thereby illustrating the system's capacity to meet demand. For numerical investigation, the proposed model is applied to a modified IEEE 14‐bus power system and a 10‐node natural gas system. Numerical results demonstrate a 9.426% reduction in investment costs and a significant 10.6% reduction in wind curtailment costs through proposed planning model. [ABSTRACT FROM AUTHOR]

Wind farm can obtain the maximize profit by optimizing micro‐locations and cables. The factors that affect profit include the power output of wind turbines, cost and et al., where power output is affected by wake effect, cable cost is related to the length and type of collector cable. The profit is calculated on the premise that the costs and power loss of collector cable are determined. Obviously, there is a hierarchical relationship between the above problems. Therefore, a bi‐level optimization model with constraints is constructed in this paper, where the upper‐level objective function is the maximum profit, and the lower‐level objective functions are consists of minimum the cable cost and the power loss of collector cable; Moreover, an improved algorithm (IDEDA), based on differential evolution and Dijkstra, is used to optimize above model; Finally, simulation experiments are carried out for IDEDA and four algorithms for two different wind conditions, and the results show that IDEDA performs better compared to the other four algorithms in terms of profit and cable cost. [ABSTRACT FROM AUTHOR]

Abstract Rapidly increasing uptake of Electric Vehicles (EVs) is expected to have a significant impact on electrical power distribution networks. Considerable work has been carried out to understand this impact and quantify the distribution networks hosting capacity, with and without network management solutions. However, the current body of knowledge does not have a comprehensive review of the research done to‐date on this topic which is vital to understand the scope of the existing studies, the data used in analysing the impact, and, most importantly, the findings. A comprehensive yet focused review of impact of EV charging on distribution networks is presented by delving into the main factors restricting EV hosting capacity and the strategies used to maximise EV hosting capacity by managing the aforementioned impacts. The authors comprehensively summarise the approaches used to quantify the impact, network and data types, and the proposed solutions to increase network hosting capacity. Moreover, the shortcomings in the existing work are identified and recommendations for future research are provided to help stakeholders understand the current state‐of‐the‐art, make informed decisions, and to be considered by future researchers.

Abstract In response to growing reliance on electricity and gas systems, this paper introduces a stochastic bi‐level model for the optimized integration of these systems. This integration is achieved through sizing and allocating of power‐to‐gas (P2G) and gas‐to‐power (G2P) units. The first level of the model focuses on decisions related to P2G and G2P unit installations, while the second level addresses optimal system operation considering decisions made from first level and stochastic scenarios. The primary aim is to enhance energy‐sharing capabilities through coupling devices and mitigate wind generation curtailment. An economic evaluation assesses the model's effectiveness in reducing costs. N − 1 contingency analysis gauges the integrated system's ability to supply load under emergency conditions. Two new indices, performance of the electricity system and performance of the natural gas system, are proposed for N − 1 contingency analysis. These indices quantify the proportion of the supplied load to the total load, thereby illustrating the system's capacity to meet demand. For numerical investigation, the proposed model is applied to a modified IEEE 14‐bus power system and a 10‐node natural gas system. Numerical results demonstrate a 9.426% reduction in investment costs and a significant 10.6% reduction in wind curtailment costs through proposed planning model.

Abstract Wind farm can obtain the maximize profit by optimizing micro‐locations and cables. The factors that affect profit include the power output of wind turbines, cost and et al., where power output is affected by wake effect, cable cost is related to the length and type of collector cable. The profit is calculated on the premise that the costs and power loss of collector cable are determined. Obviously, there is a hierarchical relationship between the above problems. Therefore, a bi‐level optimization model with constraints is constructed in this paper, where the upper‐level objective function is the maximum profit, and the lower‐level objective functions are consists of minimum the cable cost and the power loss of collector cable; Moreover, an improved algorithm (IDEDA), based on differential evolution and Dijkstra, is used to optimize above model; Finally, simulation experiments are carried out for IDEDA and four algorithms for two different wind conditions, and the results show that IDEDA performs better compared to the other four algorithms in terms of profit and cable cost.

Abstract This paper emphasizes the integration of wind and photovoltaic (PV) generation with battery energy storage systems (BESS) in distribution networks (DNs) to enhance grid sustainability, reliability, and flexibility. A novel multi‐objective optimization framework is introduced in this study to minimize energy supply costs, emissions, and energy losses while improving voltage deviation (VD) and voltage stability index (VSI). The proposed framework comprising normal boundary intersection (NBI) and decomposition‐based evolutionary algorithms (DBEA) determines the optimal siting and sizing of renewable‐based distributed resources, considering load demand variations and the intermittency of wind and solar outputs. The comparative analysis establishes that the proposed strategy performs better than many contemporary algorithms, specifically when all the objective functions are optimized simultaneously. The validation of the proposed framework was carried out on the standard IEEE‐33 bus test network, which demonstrates significant percentage savings in energy supply costs (49.6%), emission rate (62.2%), and energy loss (92.3%), along with enormous improvements in VSI (91.9%) and VD (99.8953%). The obtained results categorically underline the efficiency, reliability, and robustness of the proposed approach when employed on any complex distribution network comprising multiple renewable energy sources and battery storage systems.

This paper emphasizes the integration of wind and photovoltaic (PV) generation with battery energy storage systems (BESS) in distribution networks (DNs) to enhance grid sustainability, reliability, and flexibility. A novel multi‐objective optimization framework is introduced in this study to minimize energy supply costs, emissions, and energy losses while improving voltage deviation (VD) and voltage stability index (VSI). The proposed framework comprising normal boundary intersection (NBI) and decomposition‐based evolutionary algorithms (DBEA) determines the optimal siting and sizing of renewable‐based distributed resources, considering load demand variations and the intermittency of wind and solar outputs. The comparative analysis establishes that the proposed strategy performs better than many contemporary algorithms, specifically when all the objective functions are optimized simultaneously. The validation of the proposed framework was carried out on the standard IEEE‐33 bus test network, which demonstrates significant percentage savings in energy supply costs (49.6%), emission rate (62.2%), and energy loss (92.3%), along with enormous improvements in VSI (91.9%) and VD (99.8953%). The obtained results categorically underline the efficiency, reliability, and robustness of the proposed approach when employed on any complex distribution network comprising multiple renewable energy sources and battery storage systems. [ABSTRACT FROM AUTHOR]

Abstract In distribution networks, among the planning problems, optimal placement of medium voltage to low voltage (MV/LV) transformers is a vital and challenging issue. Electrical load uncertainty is an important factor that affects the result of this planning problem. This paper investigates optimal allocation of service transformers with respect to the load uncertainty modelled by information gap decision theory (IGDT). For this aim, the planning problem is solved in risk‐neutral (RN) and risk‐averse (RA) frameworks. In RN strategy, objective function is defined to minimize the cost of service transformers and low voltage feeders as well as the cost of power losses. On the other hand, in RA strategy, objective function is defined to maximize the radius of the uncertainty in such a way that any deviation of the uncertain parameter results in an objective function value that is not worse than the critical limit. The optimization problem is solved by crow search algorithm (CSA) and particle swarm optimization (PSO) and the results are compared. In mid‐term planning, with respect to the deviation factors of 0.05, 0.1, 0.15, 0.2, 0.25 and 0.3, optimal values of the uncertainty radius are 5.89%, 13.64%, 21.37%, 28.97%, 34.39% and 43.46%, respectively. In long‐term planning, with respect to the deviation factors of 0.05, 0.1, 0.15, 0.2, 0.25 and 0.3, optimal values of the uncertainty radius are 6.92%, 13.33%, 20.39%, 27.03%, 34% and 40.46%, respectively. Moreover, on average, CSA finds more promising results than PSO.

Abstract With the emergence of distributed energy resources (DER) and prosumers in the distribution networks, the traditional centralised operation structure has transformed into the decentralised operation and transaction structure, which is regarded as the transactive energy market. Furthermore, the planning problem in the distribution network is deeply affected by the transactive energy market. First, the literature and studies on distribution network planning with DERs are reviewed. Moreover, the multi‐stakeholder operation and planning framework under the transactive energy market are analysed. The transactive energy market provides a peer‐to‐peer (P2P) transaction for third‐party prosumers. Several techniques for multi‐stakeholders planning and P2P transaction are introduced. Finally, a tri‐layer distribution network expansion planning (DNEP) framework integrated with virtual P2P transactions is proposed and analysed to accommodate uncertainties and postpone investment. The key technology and difficulties are also discussed.

The traditional approach to planning the distribution grid has focused on reliability in the context of gradual and reasonably predictable load growth. Forecasts of load growth, combined with asset management practices, were used by system planners to identify upgrades to the system to maintain or improve reliability. The decisions, typically based within load flow analysis tools, included considerations about contingency scenarios and corporate forecasts (i.e., top-down predictions at a summary level of what would happen in a particular area that could impact load growth and behavior). Today, this traditional approach no longer fits all purposes.

In distribution networks, among the planning problems, optimal placement of medium voltage to low voltage (MV/LV) transformers is a vital and challenging issue. Electrical load uncertainty is an important factor that affects the result of this planning problem. This paper investigates optimal allocation of service transformers with respect to the load uncertainty modelled by information gap decision theory (IGDT). For this aim, the planning problem is solved in risk‐neutral (RN) and risk‐averse (RA) frameworks. In RN strategy, objective function is defined to minimize the cost of service transformers and low voltage feeders as well as the cost of power losses. On the other hand, in RA strategy, objective function is defined to maximize the radius of the uncertainty in such a way that any deviation of the uncertain parameter results in an objective function value that is not worse than the critical limit. The optimization problem is solved by crow search algorithm (CSA) and particle swarm optimization (PSO) and the results are compared. In mid‐term planning, with respect to the deviation factors of 0.05, 0.1, 0.15, 0.2, 0.25 and 0.3, optimal values of the uncertainty radius are 5.89%, 13.64%, 21.37%, 28.97%, 34.39% and 43.46%, respectively. In long‐term planning, with respect to the deviation factors of 0.05, 0.1, 0.15, 0.2, 0.25 and 0.3, optimal values of the uncertainty radius are 6.92%, 13.33%, 20.39%, 27.03%, 34% and 40.46%, respectively. Moreover, on average, CSA finds more promising results than PSO. [ABSTRACT FROM AUTHOR]

Abstract High level of photovoltaic (PV) integration into the distribution network (DN) may have a negative impact in terms of voltage variations across the limits. As such thorough analysis is required to ensure voltage within limits, mainly at feeder‐end buses. In this study, the PV integration problem is addressed in a more comprehensive way, considering the unbalance DN, impedance characteristic, and interaction of load against available PV output. The approach applies to both the planning and operational stage in the assessment of allowable PV hosting capacity (HC). In the planning stage, the local impedance index is computed at constant load and power factor conditions. The entire network is partitioned into areas and the most suitable area for PV integration is determined, which can allow maximum PV integration, without/minimum voltage violations. In the operation stage for allowable PV hosting capacity, further voltage analysis is performed, wherein peakness of PV output and load conditions do not match each other. Towards, voltage regulation via PV inverter, the power factor profile is adjusted as a function of PV output. The study is demonstrated for transient solar radiation, with emphasis on maintaining the voltage at feeder‐end buses. The simulation is carried out on interfacing GridPV in MATLAB and OpenDSS toolbox. The proposed scheme for allowable HC and further voltage regulation in the network using PV inverter is also compared with those approaches, applying optimised and centralised placement of PVs.

High level of photovoltaic (PV) integration into the distribution network (DN) may have a negative impact in terms of voltage variations across the limits. As such thorough analysis is required to ensure voltage within limits, mainly at feeder‐end buses. In this study, the PV integration problem is addressed in a more comprehensive way, considering the unbalance DN, impedance characteristic, and interaction of load against available PV output. The approach applies to both the planning and operational stage in the assessment of allowable PV hosting capacity (HC). In the planning stage, the local impedance index is computed at constant load and power factor conditions. The entire network is partitioned into areas and the most suitable area for PV integration is determined, which can allow maximum PV integration, without/minimum voltage violations. In the operation stage for allowable PV hosting capacity, further voltage analysis is performed, wherein peakness of PV output and load conditions do not match each other. Towards, voltage regulation via PV inverter, the power factor profile is adjusted as a function of PV output. The study is demonstrated for transient solar radiation, with emphasis on maintaining the voltage at feeder‐end buses. The simulation is carried out on interfacing GridPV in MATLAB and OpenDSS toolbox. The proposed scheme for allowable HC and further voltage regulation in the network using PV inverter is also compared with those approaches, applying optimised and centralised placement of PVs. [ABSTRACT FROM AUTHOR]

Abstract A novel optimal allocation framework for photovoltaic generations in an integration system of buildings‐to‐distribution‐network using improved backtracking search optimization algorithm is proposed here. In the proposed framework, photovoltaic generations are optimally allocated to optimize the overall performance of a buildings‐to‐distribution‐network regarding the efficient active power usage of photovoltaic generations, the energy savings, and voltage profile improvement of distribution network. The effects of building active demand response on the photovoltaic generations' optimal allocation are considered in the proposed framework. An improved backtracking search algorithm using two new operators is developed to optimize the active power reduction factors and locations of photovoltaic generations. The test results of IEEE 33‐bus and 69‐bus systems demonstrate that the developed framework can take full advantage of photovoltaic generation power and the active demand response of buildings to coordinate the efficient active power usage of photovoltaic generations, the voltage profile improvement and energy savings of a buildings‐to‐distribution‐network. In addition, the improved backtracking search optimization algorithm converges faster than genetic algorithm, classical backtracking search optimization algorithm, and bird swarm algorithm.

A novel optimal allocation framework for photovoltaic generations in an integration system of buildings‐to‐distribution‐network using improved backtracking search optimization algorithm is proposed here. In the proposed framework, photovoltaic generations are optimally allocated to optimize the overall performance of a buildings‐to‐distribution‐network regarding the efficient active power usage of photovoltaic generations, the energy savings, and voltage profile improvement of distribution network. The effects of building active demand response on the photovoltaic generations' optimal allocation are considered in the proposed framework. An improved backtracking search algorithm using two new operators is developed to optimize the active power reduction factors and locations of photovoltaic generations. The test results of IEEE 33‐bus and 69‐bus systems demonstrate that the developed framework can take full advantage of photovoltaic generation power and the active demand response of buildings to coordinate the efficient active power usage of photovoltaic generations, the voltage profile improvement and energy savings of a buildings‐to‐distribution‐network. In addition, the improved backtracking search optimization algorithm converges faster than genetic algorithm, classical backtracking search optimization algorithm, and bird swarm algorithm. [ABSTRACT FROM AUTHOR]

Abstract Creating 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.

Abstract In this paper, an optimal planning strategy is proposed for EV charging stations by considering travel demand based on non‐cooperative game theory. In the proposed methodology, a realistic and stochastic charging demand distribution was obtained with the travel chain of an EV and the associated probability density function. Based on the charging demand of EV owners and a multi‐operator scenario, a two‐stage effective and practical charging station planning model was established. The first stage of the planning model optimized the location of charging stations to decrease the total deadhead mileage of the EVs with the cooperation of the charging station operators. In the second stage, the operators were committed to increasing their economic benefits by developing a charging station capacity strategy in the framework of the non‐cooperative game. Furthermore, the combination of an adaptive mutation particle swarm optimization algorithm (AMPSO) and approximate Nash equilibrium had a better effect in reducing the difficulty of solving the game model and obtaining an optimal planning strategy. Finally, the results of the case study demonstrated the effectiveness of the proposed methodology for improving the charging experience of the EV owners and achieving a balance of benefits among multiple charging station operators.

The distribution grid faces several challenges related to the decarbonisation of the economy, which require incorporating flexibility services alongside traditional grid reinforcement solutions to enable an efficient grid development. Flexibility services, as well as the needs that require them, are very diverse. Therefore, general estimations about the value of flexibility applicable to any given scenario are unfeasible or imprecise. This paper reviews the literature on the quantification of the value of flexibility and proposes a broad-spectrum methodology aligned with the actual challenges of the energy transition for the planning of distribution network as it includes a comprehensive analysis of the real costs and the type of needs. Based on it, four representative and realistic case studies compare the BAU (business as usual) solutions with flexibility services analysing the technical and economic perspectives. Results show that flexibility value depends on the case studies considered and that, under certain circumstances, BAU solutions can be more competitive than alternatives with flexibility services. Network reinforcements in the distribution network have a long lifespan and provide a reliable service to thousands of customers. However, flexibility services can be particularly useful for accelerating decarbonisation with flexible connections or short-term solutions to manage the distribution network operation.

Creating 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. [ABSTRACT FROM AUTHOR]

To realize the national strategies for carbon emission reduction, electric power industries should undergo reforms to cope with the multiple challenges of decarbonization, marketization, and energy transition. How to design a dispatch strategy that considers both low‐carbon demand and economic cost has become a major concern in integrated energy systems. To realize multi‐energy complementation and low‐carbonization, a scheduling framework of combined heat and power‐virtual power plants (CHP‐VPPs) considering carbon capture and power‐to‐gas is proposed. The stochastic dynamic optimization problem is modelled as a Markov decision process. An improved deep reinforcement learning‐based algorithm named multi‐level backtracking prioritized experience replay‐twin delayed deep deterministic policy gradient (MBEPR‐TD3) is employed to solve the low‐carbon economic dispatch problem. The results show that: (1) The profits have been increased by 85.8% and carbon emissions have been reduced by 30.3% because of the addition of power‐to‐gas in CHP‐VPP; (2) the revenues have been improved by 22.24% and carbon emissions have been decreased by 37.04% owing to the introduction of carbon capture and trading; (3) compared with results using DQN, DDPG, and TD3, the optimal dispatch strategy obtained by MBEPR‐TD3 has higher reward returns and more stable convergence. [ABSTRACT FROM AUTHOR]

This study reports on the results from efforts by the Institute of Electric and Electronics Engineers (IEEE) Distribution Reliability Working Group (DRWG) and the U.S. Energy Information Administration (EIA) to improve the usefulness of reliability metrics by developing and then applying consistent, yet distinct measures of the continuity of supply based on the portion of the electric power system from which power interruptions originate: the lower voltage distribution system versus the high-voltage bulk power system. The modified metrics better support reliability planning in the US because they separately measure the effectiveness of actions to improve reliability made by the two distinct groups of firms (and their regulators or oversight bodies) that are responsible for planning and operating each portion of the US electric power system. The authors then present for the first time quantitative information on the reliability of each portion of the US electric power system. When reliability is measured using the system average interruption duration index and the system average interruption frequency index, they find that the distribution system accounts for at least 94 and 92%, respectively, of all interruptions. They also find that these relationships have been stable over the recent past.

The topic addressed in this article is part of the current concerns of modernizing power systems by promoting and implementing the concept of smart grid(s). The concepts of smart metering, a smart home, and an electric car are developing simultaneously with the idea of a smart city by developing high-performance electrical equipment and systems, telecommunications technologies, and computing and infrastructure based on artificial intelligence algorithms. The article presents contributions regarding the modeling of consumer classification and load profiling in electrical power networks and the efficiency of clustering techniques in their profiling as well as the simulation of the load of medium-voltage/low-voltage network distribution transformers to electricity meters. [ABSTRACT FROM AUTHOR]

In this paper, an optimal planning strategy is proposed for EV charging stations by considering travel demand based on non‐cooperative game theory. In the proposed methodology, a realistic and stochastic charging demand distribution was obtained with the travel chain of an EV and the associated probability density function. Based on the charging demand of EV owners and a multi‐operator scenario, a two‐stage effective and practical charging station planning model was established. The first stage of the planning model optimized the location of charging stations to decrease the total deadhead mileage of the EVs with the cooperation of the charging station operators. In the second stage, the operators were committed to increasing their economic benefits by developing a charging station capacity strategy in the framework of the non‐cooperative game. Furthermore, the combination of an adaptive mutation particle swarm optimization algorithm (AMPSO) and approximate Nash equilibrium had a better effect in reducing the difficulty of solving the game model and obtaining an optimal planning strategy. Finally, the results of the case study demonstrated the effectiveness of the proposed methodology for improving the charging experience of the EV owners and achieving a balance of benefits among multiple charging station operators. [ABSTRACT FROM AUTHOR]

Distributed energy resources (DER) as non-wires alternatives, regardless of owner, have the potential to reduce system operating costs and delay system upgrades. However, it is difficult to determine the appropriate economic signal to incentivize DER investors to install capacity that will benefit both the DER investors and the system operator. To determine this co-optimal price signal, we present a bilevel optimization framework that determines the least cost solution to distribution system over-loads. A key output of the framework is a spatiotemporal price signal to DER owners that simultaneously guarantees the DER owners' required rate of return and minimizes the system operation costs. The framework is demonstrated with a case by which the system operator considers utility owned battery energy storage systems, traditional system upgrades, and energy purchased from DER owners. The results show that utility owned storage combined with DER for non-wires alternatives can result in lower operating costs with similar upfront costs to traditional system upgrades by avoiding or delaying upgrades. In a use-case example we show that when valuing DER the operating costs over 20 years can be reduced by over 40% resulting in a $3M net present value. [Display omitted] • Valuing DER for NWA in grid planning can result in reduced distribution grid operating costs. • Novel framework is proposed to account for both DER owner and grid operator requirements. • A co-optimal price signal is determined that maximizes the social welfare of DER. • Bilevel optimization framework leverages recent advancement in linearization of bilinear terms with shadow prices. [ABSTRACT FROM AUTHOR]

Microgrids are promising to enhance power distribution systems’ efficiency, quality, sustainability, and reliability. However, microgrids operation can impose several challenges to traditional protection schemes, like changes in the power flow direction and an increase in short-circuit currents. Microgrids can include several distributed generation technologies with different behaviours during short-circuit conditions, requiring additional protection schemes and devices. In this way, the optimized coordination of reclosers and fuses in distribution networks with directional overcurrent relays, which operate as interconnection devices, might overcome many imposed protection challenges. Regarding different generation technologies, voltage-restrained overcurrent relays and frequency relays are presented as local microgrid protection for rotative and inverter-based distributed generators, respectively. The optimized coordination of these protection devices maximizes microgrid benefits and minimizes operation drawbacks by reducing interruptions impacts and energy not supplied to consumers. This work proposes, thus, a mathematical model for the optimal coordination of protection devices in distribution networks with distributed energy resources operating in grid-connected and islanded modes. The minimization technique of operating times using an elitist genetic algorithm with variable crossover and mutation processes is proposed, as well. The results show adequate coordination using passive and low-cost protection devices.

The integration of distributed generators (DGs), which are based on renewable energy sources, energy storage systems, and static VAR compensators (SVCs), requires considering more challenging operational cases due to the variability of DG production contributed by different characteristics for different time sequences. The size, quantity, technology, and location of DG units have major effects on the system to benefit from the integration. All these aspects create a multi-objective scope; therefore, it is considered a multi-objective mixed-integer optimization problem. This paper presents an improved multi-objective salp swarm optimization algorithm (MOSSA) to obtain multiple Pareto efficient solutions for the optimal number, location, and capacity of DGs and the controlling strategy of SVC a radial distribution system. MOSSA is a bio-inspired optimizer based on swarm intelligence techniques and it is used in finding the optimal solution for a global optimization problem. Two sets of objective functions have been formulated minimizing DGs and SVC cost, voltage violation, energy losses, and system emission cost. The usefulness of the proposed MOSSA has been tested with the 33-bus and 141-bus radial distribution systems and the qualitative comparisons against two well-known algorithms, multiple objective evolutionary algorithms based on decomposition (MOEA/D), and multiple objective particle swarm optimization (MOPSO) algorithm. [ABSTRACT FROM AUTHOR]

In the expansion of power distribution networks, the periodic task of determining the location, number, and capacity of the distribution transformers is still an open issue that needs to be systematically coped with. In this paper, a comprehensive approach for solving in practice the mentioned task is presented. The proposed methodology is based on an improved clustering technique, which considers not only the actual distances between consumers but also their electrical demand. Graph theory is exploited to represent the feasible paths of the complete system, so that, the distances and the obtained locations are accurate, and feasible in practice, respectively. The proposed objective function minimizes the total annual costs of the system, by considering investments, transformer losses, and operational costs. An approximate expression to calculate the maximum distance between each transformer and its furthest customer to accomplish voltage regulation has been obtained. Consequently, the outputs of the proposed model are the correct number of distribution transformers to be installed, their optimal locations and capacities, and the best consumer grouping with acceptable voltage drops. In order to validate the proposal, this methodology is applied to an urban system with 106 consumers, 23 vertexes, and 31 streets with promising results. [ABSTRACT FROM AUTHOR]

A novel distributed energy resources (DER) allocation method focused on grid constraints that avoids topological bias is proposed for distribution networks. A technology-agnostic approach is used, where a non-bias allocation of export capacity (NAEC) not specific to generation type is calculated. Moreover, the proposed NAEC is extended from an export capacity into a hosting capacity (HC) using a statistical approach. The methods are tested using the IEEE 33-bus distribution system, and two typical Irish distribution feeders -one urban, one rural- as case studies. Using a high-resolution year-long quasi-static time series simulation (QSTS) and three different generation profiles, the proposed NAEC method is validated against current practices and state of the art allocation methods in terms of active balancing, security of supply, interactions between users, operational concerns, and fairness. Results show that an equivalent or higher level of DER penetration is achieved using the proposed methodology. There are no additional constraint violations using the NAEC methodology, moreover, time slots with violations are reduced, improving security of supply. Furthermore, results suggest that avoiding topological bias makes the network accessible for more users, and prioritises self-consumption. [ABSTRACT FROM AUTHOR]

Featured Application: The paper proposes a methodology for the siting and timing of primary substations and its application to the case study of Milan, Italy. The paper aims to provide primary substations' optimal siting and timing to expand existing distribution networks. The proposed methodology relies on three main features: a geographic information system for capturing, elaborating, and displaying spatial input data; a particle swarm optimization algorithm to locate and timing the new primary substations; a Voronoi diagram-based approach to find the primary substation service areas and loading. The optimization criteria follow the approach of serving every customer from the nearest primary substation to ensure that the distribution delivery distance is as short as possible, reducing feeders' cost, electric losses, and service interruption exposure. The algorithm also considers the primary substation transformers' capacity limit. Thanks to Unareti, the distribution system operator of Milan and Brescia, the methodology was tested by carrying out several simulations, progressively increasing the number of new primary substations. The results obtained confirm the proposed approach's effectiveness and show that the methodology is a valuable tool to guide Unareti, and distribution system operators in general, in expanding distribution networks to face the challenges of the energy transition. [ABSTRACT FROM AUTHOR]

Abstract Power grid response against high‐impact low‐probability (HILP) events could be enhanced by (a) hardening mechanisms to boost its structural resilience and (b) corrective recovery and mitigation analytics to improve its operational resilience. Planning for structural resilience and attempts to find the optimal location of the Tie switches in radially operated power distribution networks that enable harnessing the network topology for maximised resilience against HILP disasters are focussed. This goal is achieved through a novel resilience‐oriented multi‐objective decision making platform, which employs a k‐PEM based probabilistic power flow (PPF) algorithm. The proposed framework offers a decision making analytic embedded with the fuzzy satisfying method (FSM) that characterises the system resilience features, such as robustness, restoration agility, load criticality, and recovered capacity, to assess different network reconfiguration options and select the optimal solution for implementation. The aforementioned resilience features are formulated in nodal level and then aggregated over the entire system to characterise the system‐level objective functions. The performance of the suggested framework is analysed on the IEEE 33‐Bus test system under a designated HILP event, and the applicability on larger networks has been verified on the IEEE 69‐bus test system. The results demonstrate the efficacy and applicability of the proposed framework in boosting the network resilience against future extremes.

The topic addressed in this article is part of the current concerns of modernizing power systems by promoting and implementing the concept of smart grid(s). The concepts of smart metering, a smart home, and an electric car are developing simultaneously with the idea of a smart city by developing high-performance electrical equipment and systems, telecommunications technologies, and computing and infrastructure based on artificial intelligence algorithms. The article presents contributions regarding the modeling of consumer classification and load profiling in electrical power networks and the efficiency of clustering techniques in their profiling as well as the simulation of the load of medium-voltage/low-voltage network distribution transformers to electricity meters.

• Optimized allocation, selectivity, coordination and specification of traditional protection and control devices (reclosers, sectionalizing switches, and fuses), in addition to island interconnection devices in the point of common coupling of microgrids, and relays ANSI 51 V and 81 as local protections in the point of coupling of distributed generation (DG) units. The proposal considers the load transference to neighbor feeders and fuse-save and fuse-blow schemes. • Inclusion of dispatchable and renewable DG and BESS since such technologies present different behaviors regarding reliability and coordination features. Electric vehicle charging stations are considered in this work, as well. • Consideration of uncertainties associated with generation and loads categorized by the k-means method. Furthermore, the state-of-charge of batteries is estimated using a fuzzy inference system, where neural network tunning techniques are applied to adjust the fuzzy sets and fuzzy rules. Associated uncertainties are used as input data, and the batteries' SoC from an optimal power flow model is considered output data for improving the fuzzy inference system performance;. • Use of a bi-level multi-criteria and multi-objective mixed integer nonlinear programming model, where the protection system coordination problem is embedded within the allocation problem as the lower-level optimization task. Efficient solutions are generated by NSGA-II metaheuristic, and the best compromise solution is identified by using the compromise programming based on companies' criteria in a weight system, where the cost of energy not supplied and investment cost are the conflicting objectives. Microgrids are able to improve several features of power systems, such as energy efficiencies, operating costs and environmental impacts. Nevertheless, microgrids' protection must work congruently with power distribution protection to safely take all advantages. This research contributes to enable their protection by proposing a bi-level method to simultaneously solve the allocation and coordination problems, where the proposed scheme also includes local protections of distributed energy resources. The uncertainties associated with generation and loads are categorized by the k-means method, as well. The non-dominated sorting genetic algorithm II is employed in the upper-level task to solve the protection and control devices allocation problem with two opposing objectives. In the lower-level task, a genetic algorithm ensures their coordination. Protection devices include reclosers and fuses from the network, and directional relays for the point of common coupling of microgrids, while control devices consist of remote-controlled switches. In contrast to related works, local devices installed at the point of coupling of distributed generation units are considered as well, such as voltage-restrained overcurrent relays and frequency relays. The optimal solution for the decision-maker is achieved by utilizing the compromise programming technique. Results show the importance of solving the allocation and coordination problems simultaneously, achieving up to $25,000 cost savings compared to cases that solve these problems separately. The integrated strategy allows the network operator to select the optimum solution for the protective system and avoid corrective actions afterward. The results also show the viability of the islanding operation depending on the decision maker's criteria. [ABSTRACT FROM AUTHOR]

The paper aims to provide primary substations’ optimal siting and timing to expand existing distribution networks. The proposed methodology relies on three main features: a geographic information system for capturing, elaborating, and displaying spatial input data; a particle swarm optimization algorithm to locate and timing the new primary substations; a Voronoi diagram-based approach to find the primary substation service areas and loading. The optimization criteria follow the approach of serving every customer from the nearest primary substation to ensure that the distribution delivery distance is as short as possible, reducing feeders’ cost, electric losses, and service interruption exposure. The algorithm also considers the primary substation transformers’ capacity limit. Thanks to Unareti, the distribution system operator of Milan and Brescia, the methodology was tested by carrying out several simulations, progressively increasing the number of new primary substations. The results obtained confirm the proposed approach’s effectiveness and show that the methodology is a valuable tool to guide Unareti, and distribution system operators in general, in expanding distribution networks to face the challenges of the energy transition.

Routine inspection programs are procedures that utilities can use to improve their power quality standards and public perception. However, due to budget restrictions, it is usually not possible to inspect all elements in the whole concession area. In this scenario, strategies to efficiently allocate available teams at scheduled inspections are an interesting alternative. The challenge is to define which data are relevant and at hand to develop an strategy that is comprehensive, efficient and outperforms the current old fashioned way based on personal experience applied to automated spreadsheets. This paper proposes an approach that deals with a large amount of data from corporate systems as input to a proposed optimization engine based on Genetic Algorithm and heuristics. Primary feeders are split into segments among protection devices, referred to as metazones. The developed tool produces a set of prioritized metazones by maximizing the sum of merit indexes related to loss of energy, voltage and current violations, as well as outages, while satisfying teams’ inspections capacities. A case study with 56 primary feeders is presented, and results show that the yielded prioritized metazones are compatible with and improve current power utilities practices. [ABSTRACT FROM AUTHOR]

With the integration of more and more renewable energy generations (REGs), the structure of traditional distribution networks is hard to accommodate the volatile power injections of REGs. As a new power electronic device, soft open point (SOP) can be installed to control both active and reactive power flow among active distribution networks (ADNs). This paper presents a comprehensive optimization method for allocating SOPs within an ADN with high penetration of REGs. In order to find proper SOP candidate locations, a selection strategy based on two technical indices is proposed. To mitigate the risk of voltage violation caused by REG forecast errors and improve the adaptiveness of allocation results, a two-stage robust optimization model for SOP allocation is formulated to minimize the total cost of SOP investment and network operation. The proposed model is converted into a mixed-integer second-order cone programming (MISCOP) problem, which is then decoupled into a master problem of planning and a subproblem of operation and solved by column and constraint generation (CCG) algorithm. Simulation results show that the proposed method can effectively find the optimal SOP allocation schemes. Comparisons with different mathematical formulation and solution methods show the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

This study presents a novel framework for investment prioritisation in a distribution network by performing a flexibility needs assessment with regards to power quality parameters. Power range and time instances of activation are identified with a view of maintaining normal operation. A step forward is made by incorporating the importance of the flexibility needs assessment in investment prioritisation as part of the network expansion planning. The proposed framework consists of three procedures: input data preparation, operational calculations and the post-processing of results, which are used to quantify the flexibility needs and propose an investment prioritisation list. A bus node, which forms a part of the existing distribution network in Slovenia, is used to demonstrate the general applicability of the framework. As a result, an investment prioritisation list was compiled by assessing the flexibility needs. The secure power supply buffer of the node is determined, while the quantification of the flexibility power range and time instances of activation are also provided in order to further mitigate network constraints. Apart from providing valuable information to network operators, the newly proposed framework lays the foundation for aggregators and market players to plan and self-balance their portfolio and position when providing flexibility market services. [ABSTRACT FROM AUTHOR]

To coordinate the increasing application of electric vehicles (EVs), the deployment of charging stations becomes vital for supporting the charging and navigation for EVs, which also affects the reliability of the corresponding electricity network. To achieve the balanced reliability in both the electricity network and EV trip, an optimal planning method for charging stations in the electricity‐transportation coupled networks is proposed. Firstly, an integrated management strategy of EVs is demonstrated to model the reliability enhancement of the two networks. Thereafter, a coupled network reliability assessment method based on the quasi‐sequential Monte Carlo simulation is presented. This study proposes a novel reliability‐oriented multi‐objective optimal planning model for charging stations planning problem, which is resolved by implementing the optimal planning scheme from the candidate nodes. Additionally, correlation analysis is performed to ensure that the proposed model can be solved quickly for large‐scale electricity network with complex transportation flow input data. A test case is presented and simulated to validate the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The rapid rise in the grid integration of low‐carbon technologies, e.g. renewable energy sources (RESs) and plug‐in electric vehicles (PEVs), has led to several challenges in distribution networks (DNs). This is due to the intermittent generation of RESs and uncertain loads of PEVs, both of which necessitate enhancing the flexibility requirements at the distribution level so as to accommodate the high penetration of these clean technologies in the future. To address such issues, this study proposes a mixed‐integer linear programming‐based expansion planning model for DNs considering the impact of high RES and PEV penetration, the associated uncertainties, and providing flexibility requirements at the distribution level. In this respect, the spherical simplex unscented transformation, an analytical uncertainty modelling method, is implemented in the planning model to take into account the forecasting errors of the uncertain green technologies. Also, in order to estimate the electric vehicle parking lot demand at each load node of the network, a new approach for PEV‐charging model is suggested. To investigate the effectiveness and efficiency of the proposed probabilistic planning model, it is implemented on two test DNs, and the obtained results are thoroughly discussed. [ABSTRACT FROM AUTHOR]

Soft open points (SOPs) have the ability to regulate the power flow among their terminals in a continuous manner, which can solve the problems brought by the synergy of distribution networks and distributed generation, and improve the system reliability and power supply capability. To address the current lack of quantitative measurement on the effects of SOPs, the power supply capability evaluation method for active distribution networks (ADNs) with four-terminal SOPs considering reliability is proposed. Firstly, the topology and configuration modes of four-terminal SOPs are studied, and their control modes are investigated in normal operation and supply restoration conditions. Then, using the feeder partition method, the effects of four-terminal SOPs on the power outage duration of different load areas after a fault are studied, and the reliability evaluation process for ADNs with four-terminal SOPs is developed based on the quasi-Monte Carlo method. Later, with reliability as the main constraint, the power supply capability evaluation model for ADNs with four-terminal SOPs is established, and the solution algorithm is proposed. Finally, the effectiveness and applicability of the method proposed are verified through a case study.

In this paper, power distribution planning (PDP) considering distributed generators (DGs) is investigated as a dynamic multi-objective optimization problem. Moreover, Monte Carlo simulation (MCS) is applied to handle the uncertainty in electricity price and load demand. In the proposed model, investment and operation costs, losses and purchased power from the main grid are incorporated in the first objective function, while pollution emission due to DGs and the grid is considered in the second objective function. One of the important advantages of the proposed objective function is a feeder and substation expansion in addition to an optimal placement of DGs. The resulted model is a mixed-integer non-linear one, which is solved using a non-dominated sorting improved harmony search algorithm (NSIHSA). As multi-objective optimization problems do not have a unique solution, to obtain the final optimum solution, fuzzy decision making analysis tagged with planner criteria is applied. To show the effectiveness of the proposed model and its solution, it is applied to a 9-node distribution system.

Most distribution network planning models rely on predefined candidate electric line routes (branches), i.e. the planning model is to select a subset of the candidate branches to form a network with minimal investment cost and guarantee the power supply to consumers with specified reliability. However, the manually presented candidate branch set may be thoughtless while the exhaustively generated a large set of candidate branches may make the planning model intractable. In this study, the authors propose a distribution network planning model based on feeder corridors instead of the candidate branches, since feeder corridors are commonly given in municipal planning. Based on the graph model formulated by feeder corridors, the proposed planning model can generate the network with minimal investment cost while meeting the specific reliability requirement. Furthermore, since post-fault load restoration strategies between feeders are fully incorporated, the proposed model is applicable for mesh networks. The planning model is formulated as a mixed-integer linear programming (MILP) problem and can be effectively solved by off-the-shelf software. Simulation results show the effectiveness and flexibility of the proposed method. [ABSTRACT FROM AUTHOR]

During the power system restoration there is a high possibility that the load demand is greater than the normal system operation for thermostatic loads. This demand increment, called Cold Load Pick-up (CLPU) has been a critical concern to utilities and will slow down the restoration process. Thus, it is imperative to predict the CLPU demand to perform restoration process more precisely. The use of new loads such as electric vehicles (EVs) has made new problems. In this paper, first the negative impact of EVs after an outage, on CLPU demand is investigated and then it is shown that the behavior of EVs during restoration service is the same as CLPU phenomenon. In doing so, the proposed approach uses Monte Carlo simulation method to predict EVs recharging demand. Since there will be some other new loads in the future that may have CLPU performance, a new approach is proposed to predict the CLPU demand of any load, without the need to have its exact model. ANFIS method is used to estimate CLPU demand based on previous outage cases in the feeder. The proposed approach is implemented in MATLAB® and simulation results confirm its ability in load prediction for restoration service. [ABSTRACT FROM AUTHOR]

With the increasing penetration of electronic loads and distributed energy resources, conventional load models cannot capture their dynamics. Therefore, a new comprehensive composite load model is developed by Western Electricity Coordinating Council (WECC). However, this model is a complex high‐order non‐linear system with multi‐time‐scale property, which poses challenges on power system studies with heavy computational burden. In order to reduce the model complexity, the authors firstly develop a large‐signal order reduction (LSOR) method using singular perturbation theory. In this method, the fast dynamics are integrated into the slow ones to preserve transient characteristics of the former. Then, accuracy assessment conditions are proposed and embedded into the LSOR to improve and guarantee the accuracy of reduced‐order model. Finally, the reduced‐order WECC composite load model is derived by using the proposed algorithm. Simulation results show that the reduced‐order large‐signal model significantly alleviates the computational burden while maintaining similar dynamic responses as the original composite load model. [ABSTRACT FROM AUTHOR]

Optimal placement of protective devices and switches in the distribution system improves the system reliability at the cost of increased investment. In this study, a new bi‐directional analytical model has been developed to solve the problem of optimal placement of protective devices and switches in various zones of a distribution system with distributed generation (DG). The model determines the optimal number and locations of reclosers, switches, 'fuse‐save fuses' and 'fuse‐blow fuses' for increasing the system reliability while reducing the investment and outage costs, considering system data uncertainties. For considering uncertainties in load data, failure rates (temporary and permanent) and repair rates, the model utilises three‐point estimate method. The formulated problem has been solved for 69‐bus distribution system and radial distribution system connected to bus 4 of the reliability busbar test system, using genetic algorithm optimisation technique. After analysing the test results, it is concluded that considerable profit to the utility is achieved by optimal placement of protective devices and switches in various zones of the distribution system with DG units. [ABSTRACT FROM AUTHOR]

This study aims to address this issue by improving the social, economic and technical objective functions in an automated distribution system. Optimal switching is regarded as a strategic approach for satisfying these goals. Due to the growing popularity of the renewable energy sources of wind type, several wind turbines are considered in the grid. Such a grid structure requires a powerful and reliable framework which can consider the correlation effects between wind turbines and run optimal switching to reconfigure the grid, simultaneously. Therefore, a novel stochastic framework based on scenario reduction is deployed which can model the uncertainties of the wind turbines, market price and load demands in the operation time horizon. The objective functions to be optimised are the total automated grid costs as the economic function, and the total grid emission and the system average interruption duration index as the social functions. This is a complex non‐linear and mixed integer problem which needs a global optimiser to get solved. Therefore, an efficient and accurate heuristic optimisation algorithm‐based crow search is proposed to solve the problem. A two‐stage modification method is also proposed which improves the crow search algorithm performance using an IEEE test system. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Resiliently designed and constructed integrated gas‐electric distribution networks (GEDNs) against natural disasters are crucial to social welfare. In this study, a two‐stage robust optimisation‐based co‐expansion planning model is proposed to attain an integrated GEDN with a given resilience level, by optimising the investment strategies of hardening and selective expansion of power distribution feeders and natural gas pipelines, as well as the location and capacity of natural‐gas‐fired distributed generation. In the first stage, the overall annual investment and operation cost is minimised under normal operation conditions while in the second stage, the feasibility of the investment decisions under the identified worst‐case natural disaster scenario is checked with an adjustable load shedding cost criterion. The proposed model is formulated as a mixed integer second‐order cone programming problem with the column and constraint generation algorithm employed to seek the optimal solution. Case studies on two integrated GEDNs demonstrate the performance of the proposed methodology. [ABSTRACT FROM AUTHOR]

The expansion planning of a distribution network (DN) is studied separately and independently from the behaviour of the transmission network (TN) as well as other DNs. However, DNs are being converted from passive to active mode by extensively integrating distributed generations (DGs) into them. Hence, an uncoordinated expansion planning may lead to redundant investment cost and suboptimal solutions. Accordingly, the main motivation of this study is to propose a coordinated expansion scheme among distribution companies (DISCOs). In doing so, a novel bilevel game-based framework is presented to coordinate the decisions of DISCOs for distribution expansion planning problem. Primal–dual formulation is used to convert the bilevel problem into a single level one resulting in a discretely-constrained generalised Nash equilibrium problem. The generalised Nash equilibrium problem is further formulated as a mixed-integer linear program using Karush–Kuhn–Tucker conditions. The methodology proposed is applied to the IEEE 24-bus RTS with several 24-bus DNs. The results show that ignoring the action of other DISCOs may lead to the over- or under-investment in DG sector which consequently decreases the DISCOs' profit. The results also show that congestion in transmission lines can considerably affect the strategic decisions of DISCOs on DG investment. [ABSTRACT FROM AUTHOR]

A microgrid (MG) is a cluster of small‐scale sources known as micro‐sources, the energy storage devices, and the loads. MGs are promising entities that allow a considerable amount of renewable energy penetration into the system. A stochastic optimisation model is presented in this study to find the optimal size of the distributed energy resources (DERs). The uncertainties are considered with the help of multiple scenarios of random variables viz. wind output, solar output, and load demand data. A particular case of Beta distribution is used to create these variations. A cost‐based, multi‐variable constrained non‐linear objective function is formulated. The considered test MG system is more generalized with solar and wind energy penetration. The results show the effect of operational as well as the planning aspects together under various reliability conditions for an isolated MG. The formulated problem is solved to obtain a global optimal solution using a sequential programming approach and a comparative assessment of results with a hybrid particle swarm optimisation (PSO) approach has also been presented. It is found that the results obtained from the sequential programming approach are better compared to the hybrid PSO approach. [ABSTRACT FROM AUTHOR]