Load Curtailment Research Articles

Practical solutions for microgrid energy management: Integrating solar forecasting and correction algorithms

Nowadays, the shift towards renewable energies is happening at an exponential pace. Microgrids are an integral part in integrating renewable energies into the global energy mix. Due to the volatility of renewables, such as solar or wind, proper energy management is needed to avoid generation or load curtailment. This issue is addressed throughout the literature using a wide variety of strategies, ranging from classical linear to nature inspired metaheuristic optimization algorithms. This paper goes beyond the simulation phase of different optimization algorithms and addresses the optimal energy management problem by proposing a novel framework to integrate optimization strategies into the daily operation of microgrids. The proposed framework showcases 3 practical methods for controlling the distributed generators, as well as a communication scheme which facilitates the data transfer between the machine running the optimization strategy and the energy management system of the microgrid. The framework is demonstrated on a small-scale islanded microgrid setup, located at the Technical University of Cluj-Napoca. The microgrid setup consists of a photovoltaic array, a battery energy storage system and two dispatchable generators. A two-stage optimization strategy is used, consisting of a day-ahead stage, which uses the solar forecast provided by the Global Forecast System, prior to each day, to issue a working plan for the dispatchable generators, and an intra-day stage, which uses hourly solar forecasts, issued during the day, to adjust the operation of the microgrid and minimize the error between the day-ahead solar forecast and the actual weather conditions. Mixed integer linear programming is used to solve the optimization problem and validate the correct operation of the proposed framework over two case studies. The results show that the microgrid works as intended, in both scenarios, as well as the benefits of using an intra-day correction stage.

A collaborative restoration strategy of resilient distribution system with the support of electric bus clusters

The widespread deployment of electric buses (EBs) offers substantial flexibility resources to support the service restoration and enhance the resilience of distribution system (DS). Therefore, this paper presents a comprehensive and collaborative service restoration strategy for resilient DS, considering the support of EBs. First, the restoration flexibility model for EBs is derived, which quantifies the restoration capability of EBs by dividing effective EB clusters. This model is integrated into the DS restoration process. Then, a two-step collaborative restoration method of DS is proposed with the aim of minimizing the load curtailment cost. The first step addresses the restoration planning problem to determine the dispatch decisions for EB clusters. Based on above planning solutions, the second step focuses on the real-time restoration problem of DS, obtaining optimal restoration results through coordinating EB clusters and other local resources. In this stage, the impact of natural disasters on the EB cluster dispatch in transportation system is considered, and a rolling optimization method is employed to deal with uncertainties from renewables, load, and EB cluster dispatch time. Finally, numerical simulations are conducted using the modified IEEE 33-bus distribution test system and a 29-node transportation system to verify the effectiveness of the proposed restoration method.

Optimal charging of electric vehicles in smart stations and its effects on the distribution network using meerkat optimization algorithm

The proliferation of electric vehicles (EVs) presents a significant challenge and opportunity for the energy sector. This study proposes a novel approach for optimizing EV charging within smart stations, considering its impact on the distribution network. Leveraging the Meerkat Optimization Algorithm (MOA), we address the complex optimization problem of balancing EV charging demands with grid constraints. Navigating distribution grid energy management complexities, including renewable resources and dynamic demand, is challenging. We introduce a sophisticated optimization model tailored for grid operations, featuring meticulous formulations for energy management. The model optimizes battery usage, EV energy management, compensator utilization, and distributed generation dispatch. Through extensive simulations, we demonstrate the approach's effectiveness in minimizing charging costs, reducing grid congestion, and enhancing overall system performance. The multi-objective function minimizes energy losses, power purchases, load curtailment, distributed generation, and battery/EV expenses over 24 h. Simulations validate a significant reduction in the distribution grid's operating cost. This research highlights the potential of advanced optimization techniques in smart charging infrastructure to facilitate widespread EV adoption while ensuring grid reliability and efficiency. Incorporating electric vehicles (EVs) into the system yields significant improvements across performance indicators compared to scenarios without EVs. Results indicate a 19 % reduction in the objective function value, with a notable 74 % decrease in energy purchase and a 60 % reduction in energy losses. Additionally, load shedding decreases by approximately 75 %, while voltage deviation decreases by around 44 %. Importantly, no PV or WD curtailment is observed with EV integration, showcasing its compatibility with renewable energy generation profiles and emphasizing its potential to enhance system efficiency, reliability, and sustainability.

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

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

Unlocking responsive flexibility within local energy communities in the presence of grid-scale batteries

The transition towards a decentralized, decarbonized, and distributed energy infrastructure necessitates techno-economic initiatives to empower local energy communities (LECs) to achieve self-reliance and evolve into self-sustained electricity networks. It is crucial to underscore the significance of network resilience, especially in the context of local power generation, battery storage, and the radial topology of low-voltage (LV) networks. While contemporary LV networks have made significant attempts to integrate distributed energy resources (DERs), the notable deficiency lies in their lack of network redundancy, posing a substantial challenge in the occurrence of high-impact, low-probability (HILP) events. Therefore, to enhance LV network resilience and leverage its capability to withstand unexpected disruptions, the network operator needs to unlock the potential contributions of end-users within the active distribution networks (ADNs). In this paper, a comprehensive model is developed based on multi-temporal optimal power flow (MTOPF) for unbalanced LV networks addressing the technical issues in islanded microgrid operational planning. The contributions of the grid-scale batteries in forming islanded microgrids and the flexibility that can be provided by the end-users in the LEC have been considered in this paper. To demonstrate the performance of the proposed model, the simulation studies have been carried out on a part of medium and low voltage networks, consisting of network reconfiguration and load transferring capability to reduce the service interruptions during HILP events. The energy-not-served (ENS) is chosen as one of the key performance indicators (KPIs) in this study. With the unlocking flexibility potentials and contribution of the DERs, including grid-scale energy storage (GES) units and Photovoltaic (PV) panels, the ENS has been reduced from 700.8 kWh to 447.5 kWh by activating the local resources, proper switching action, and contribution of the flexible loads, for one of the severe HILP events, i.e., the main grid outage. In this case, the full load curtailment index is reduced from 180 to 106 client hours.

End-to-end learning for stochastic preventive dispatch of renewables-rich power systems in abnormal weather conditions

Power systems are currently increasingly threatened by abnormal weather conditions, which are exacerbated by global warming. In particular, the proliferation of renewable energy sources (RES), which are quite variable, might cause power systems to have insufficient energy supply to cope with the risk of severe power imbalances in such conditions. In this paper, a preventive power system dispatch model is proposed that allows power systems with large RES (specifically wind energy) installations to mitigate excessive load curtailments under abnormal weather conditions. Compared to the conventional predict-then-optimize (PTO) framework of power system dispatch that relies on accurate RES forecasting, the proposed end-to-end learning framework mitigates the impact of forecasting errors on decision-making, which reduces the dependence on the accuracy of RES forecasting and effectively enhances the resilience of the power system. The proposed framework consists of a probabilistic RES forecasting module, a preventive dispatch module, and a resilience-oriented assessment module. A backpropagation mechanism based on Karush-Kuhn-Tucker conditions is proposed to unify the dispatch optimization problem as deep neural networks, which ensures the reliability of optimal solutions as well as provides necessary gradients for backpropagation, successfully bridging the information gap among the three modules. The proposed model is updated iteratively in an online implementation scheme composed of the decision-making part and the reflection & training part. The results of numerical experiments on the IEEE 6- and 118-bus systems verify the effectiveness and superiority of the proposed model in maintaining the resilience of RES-based power systems.

Interpreting the value of flexibility in AC security-constrained transmission expansion planning via a cooperative game framework

Security-constrained transmission expansion planning (SCTEP) is an inherently complex problem that requires simultaneously solving multiple contingency states of the system (usually corresponding to N-1 security criterion). Existing studies focus on effectively finding optimal solutions; however, single optimal solutions are not sufficient to interpret the value of flexibility (e.g., from energy storage systems) and support system planners in well-informed decision making. In view of planning uncertainties, it is necessary to estimate the contributions of flexibility to various objectives and prioritise the most effective investments. In this regard, this work introduces a SCTEP tool that enables interpreting the value of flexibility in terms of contributions to avoided load curtailment and total expected system cost reduction. Inspired by cooperative game theory, the tool ranks the contributions of flexibility providers and compares them against traditional line reinforcements. This information can be used by system planners to prioritise investments with higher contributions and synergistic capabilities.

Deep reinforcement learning based energy management of a hybrid electricity-heat-hydrogen energy system with demand response

Hybrid electricity-heat-hydrogen energy system with demand response (DR) is promising in enhancing flexibility and energy efficiency. However, the multi-energy coupling and source-load uncertainties makes it challenging to efficiently schedule energy flows of electricity generation, storage and DR. To this end, this paper proposes a continues deep reinforcement learning algorithm, specifically the deep deterministic policy gradient (DDPG), for the energy management optimization. Different Markov decision processes are firstly employed to analyze and compare two kinds of incentive-based electro-thermal DR contracts, i.e., load curtailment and load shifting. Simulation results exemplify the superiority of the proposed DDPG-based scheduling incorporating electro-thermal DR in terms of economy and sustainability, leading to a 16.02 % reduction in scheduling costs for contract load curtailment and an 8.52 % reduction for contract load shifting when compared to that without DR consideration. Furthermore, the robustness of DDPG-based scheduling is verified under 60 random source-load scenarios compared with different algorithms. Compared to results obtained by DDPG, DDPG-LC and DDPG-LS reduce the mean cost by 22.15 % and 12.84 %, while their error from the theoretical optimum is only around 5 %. The results demonstrate that the approximate optimality and rapid decision-making illustrate DDPG's efficient real-time scheduling capability, thereby enhancing the system's adaptability to uncertain environments.

A multi-objective energy scheduling of the reconfigurable off-grid microgrid with electric vehicles using demand response program

Microgrids are essential in ensuring responsible energy consumption and production patterns to support and advance the United Nations’ sustainable development goals. However, due to the limited supply in the off-grid microgrids, an increase in the integration of electric vehicles (EVs) into electrical networks for their charging operation is expected to create a void between the energy supply and demand. Demand response (DR) programs provide a better solution to the energy management problem of microgrids. However, the difference in the objectives of the stakeholders of the off-grid microgrid can create an issue in providing optimal solutions to its energy scheduling problem. This paper focuses on the multi-objective energy scheduling strategy of the off-grid microgrid to manage its limited energy supply optimally using an incentive-based DR program. A fast heuristic approach is also presented in this work to reconfigure off-grid microgrid optimally during its hourly operations. Furthermore, Hong’s (2m+1) point estimation method is used in this work to consider the uncertainties in the EVs and other DR participants. The proposed work is analyzed on the 33-bus and 69-bus off-grid microgrid consisting of droop-controlled distributed generations, EV charging stations, and consumers with curtailable loads. For the day-ahead operation of the off-grid microgrids, the analysis shows that the proposed work reduces the operational cost of the two off-grid microgrids by 2.3% and 4.56%, respectively.

Revolutionizing energy management: A two-step optimal day-ahead scheduling approach for advanced distribution systems with enhanced transactional efficiency

This study makes a significant contribution to advancing smart energy management systems, crucial for effective control of energy supply and demand. Traditional methods for optimal energy management in power grids often encounter computational challenges due to the nonline ar and discrete nature of the optimization problem. In response, our research introduces a novel two-step Optimal Day-Ahead Scheduling approach for an Advanced Distribution System. This system incorporates Renewable Energy Sources, Distributed Generation units, Electric Vehicles, and Energy Storage Systems, while also facilitating surplus electricity sales to electricity markets. The proposed optimal day-ahead scheduling involves both central and local controllers collaborating to optimize a unified program. The Active Distribution System engages in electricity transactions with multiple descending energy hubs, including Combined Cooling, Heating, and Power plants. Each energy hub (EH) operator optimizes day-ahead scheduling problems, submitting bids to the distribution system operator. This unique approach integrates Demand Load Curtailment and Time-of-Use schemes as demand response programs in descending energy hubs. Addressing the inherent complexity of this optimization challenge, the study introduces a novel path-finding algorithm leveraging an improved local search operator. The application of this proposed method to a 33-bus system demonstrates significant advantages. Results showcase a notable increase of up to 193% in the benefit of selling electricity, coupled with an impressive 84% reduction in distribution system operating costs, considering base values. This contribution highlights the effectiveness of the proposed approach in optimizing energy transactions and enhancing the overall efficiency of distribution system operations.

Embedded Real-Swarm Evolutionary Programming Technique for Intelligent Load Curtailment Strategy

Some traditional optimization techniques are inaccurate and failed to reach their optimal solutions since the solutions normally stuck at local optimal. Thus, any optimization technique cannot be generalized as a reliable optimizer since some optimization techniques are unique to solve optimization problems. This may also occur in power system optimization problem. Load curtailment is one of the important issues in power systems since its approach can help control the power system loss. In general, it is termed as loss minimization so that the delivery of electricity to the consumers can be smoothened. This paper proposes a new optimization technique, Embedded Real Swarm Evolutionary Programming (ERSEP) for identify contingencies occurrence in power system. ERSEP is the integration of real mutation swarm operator with the traditional evolutionary programming (EP) which aims to produce better results in terms of achieving lower optimal solution. Comparative studies were conducted to observe the advantages of ERSEP over the traditional. Results exhibited that the proposed ERSEP outperformed the traditional EP in achieving lower optimal solution validated on IEEE 30-Bus Reliability Test System (RTS). Significant results deduced from this study revealed that total transmission loss reduction worth 52.83% was achieved by EP, 54.09% solved by PSO and 74.09% by ERSEP in Case 1 for chosen load condition. In Case 2, ERSEP maintains to achieve the highest loss reduction worth 54.97%, while EP achieved 51.03% and PSO achieved 52.98% loss reduction. ERSEP maintains to achieve highest loss reduction worth 61.63%, while EP achieved 51.03% and PSO achieved 52.98% loss reduction. This implies that ERSEP is superior in all cases to reach the lowest minimized transmission loss.

Convex Relaxations of Maximal Load Delivery for Multi-Contingency Analysis of Joint Electric Power and Natural Gas Transmission Networks

Recent increases in gas-fired power generation have engendered increased interdependencies between natural gas and power transmission systems. These interdependencies have amplified existing vulnerabilities in gas and power grids, where disruptions can require the curtailment of load in one or both systems. Although typically operated independently, coordination of these systems during severe disruptions can allow for targeted delivery to lifeline services, including gas delivery for residential heating and power delivery for critical facilities. To address the challenge of estimating maximum joint network capacities under such disruptions, we consider the task of determining feasible steady-state operating points for severely damaged systems while ensuring the maximal delivery of gas and power loads simultaneously, represented mathematically as the nonconvex joint Maximal Load Delivery (MLD) problem. To increase its tractability, we present a mixed-integer convex relaxation of the MLD problem. Then, to demonstrate the relaxation’s effectiveness in determining bounds on network capacities, exact and relaxed MLD formulations are compared across various multi-contingency scenarios on nine joint networks ranging in size from 25 to 1191 nodes. The relaxation-based methodology is observed to accurately and efficiently estimate the impacts of severe joint network disruptions, often converging to the relaxed MLD problem’s globally optimal solution within ten seconds.

Optimal demand response aggregation in wholesale electricity markets: Comparative analysis of polyhedral; ellipsoidal and box methods for modeling uncertainties

This paper presents an optimal framework for demand response aggregators in wholesale electricity markets. Demand response aggregators provide customers with flexible contracts in the proposed model. These contracts allow for hourly load reductions through load curtailment, load shifting, onsite generation utilization, and energy storage systems. The study suggests the price-based self-scheduling model for demand response aggregators, which seeks to maximize the aggregator's payoff for participation in day-ahead energy markets. The proposed model is implemented on a sample demand response aggregator with uncertainty and without uncertainty. Three methods, box, polyhedral, and ellipsoidal, based on robust optimization, are proposed for uncertainty modeling in this work, and their effectiveness is compared with each other. The results show that if the uncertainty is modeled using the polyhedral method, the value of the objective function will deviate by only 22.72 % compared to the case without uncertainty. However, this deviation in box and ellipsoidal methods is 118.48 % and 49.20 %, respectively, which shows the superiority of the polyhedral method compared to the oval and box methods.

Reliability evaluation technique with the integration of wind power fluctuation through considering frequency regulation and failure restoration

The adequacy of power system can be elevated with the increasing grid-connected capacity of wind farm, and meanwhile, the power system reliability is also affected by the intermittent power fluctuation of wind farm. However, the traditional reliability evaluation technique cannot take the power fluctuation into account. Therefore, for considering the positive and negative influence of wind energy on the power system reliability, this paper proposes a reliability evaluation technique through considering static frequency regulation and failure restoration process. A failure restoration strategy which can be integrated into the reliability evaluation model is presented through designing system load curtailment, system frequency restoration, and system load restoration strategies. A series reliability calculating models which depended on the different failure restoration conditions are established. Case studies for different systems are investigated, and the results verify that the proposed method can consider both aspects of the positive and negative influence of wind farm on the power system reliability.

Reliability Assessment for Integrated Seaport Energy System Considering Multiple Thermal Inertia

With the rapid development of global trade, a large number of goods and resources are imported and exported via seaports. Multiple thermal loads and renewable energy merge into seaports, making the energy supply and demand structure increasingly complex. The traditional seaport becomes an integrated seaport energy system (ISES). Due to the complicated energy interaction of cooling, heating, electricity, and gas subsystems, the ISES urgently require reliable and secure operation. Hence, this paper proposes a new reliability assessment method for the ISES that considers thermal inertia. Firstly, the operational structure of the ISES is established considering multi-energy coupling relationships. Then, a two-stage optimal load curtailment model is constructed with multiple thermal inertias. In addition, the reliability assessment method for the ISES is proposed based on the sequential Monte Carlo simulation method. Simulations are presented to verify the effectiveness of the proposed method.

Enhanced energy balancing and optimal load curtailment strategy for DC microgrid integration in hybrid AC/DC distribution networks

AbstractUnleashing the potential of distributed renewable energy sources (RESs), intelligent and autonomous microgrids are becoming pivotal in attaining net‐zero carbon emission goals. Hybrid AC/DC microgrids rise as cutting‐edge microgrid topologies, capitalising on the best of both AC and DC systems. However, the integration of intermittent renewables and uncertainties in loading poses stability challenges. Advanced bidirectional converter controls provide efficient power exchange, but in extreme contingencies, a resilient supervisory control framework (load management/load curtailment approach) is inevitable to withstand/avoid unplanned renewable disruptions/blackouts. Moreover, the operational paradigm shift towards achieving net‐zero emissions, isolated operation of RESs, and conventional load shedding methods are anticipated to encounter substantial challenges, necessitating the development of alternative strategies. In order to improve the stability of hybrid microgrid systems in islanding scenarios, this research presents an energy balancing and load curtailment strategy. The proposed method aims at optimising resource utilisation, prioritising essential loads, and executing an optimal load curtailment strategy (if required), thereby augmenting the stability of systems. Unlike a meta‐heuristic or exhaustive search, which depends on 2n − 1 possible combinations and become unworkable as load numbers increase, the suggested methodology is based on a mathematically modelled load restriction method. By including load criticality, this strategy effectively prevents blackouts even with an increasing number of loads, providing a significantly more useful and practical solution. Additionally, the proposed charging algorithm ensures that the energy storage system imports energy from the grid during off‐peak hours and maximises power generation from the DC subgrid. The efficacy of the proposed strategy is validated using a modified IEEE‐33 bus system as a test case for a hybrid AC/DC microgrid. Simulation results demonstrate the effectiveness of the MILP‐based load curtailment approach in maintaining system stability and preventing blackouts during unforeseen events.

A Resilient Integrated Resource Planning Framework for Transmission Systems: Analysis and Optimization

This article presents a resilient Integrated Resource Planning (IRP) framework designed for transmission systems, with a specific focus on analyzing and optimizing responses to High-Impact Low-Probability (HILP) events. The framework aims to improve the resilience of transmission networks in the face of extreme events by prioritizing the assessment of events with significant consequences. Unlike traditional reliability-based planning methods that average the impact of various outage durations, this work adopts a metric based on the proximity of outage lines to generators to select HILP events. The system’s baseline resilience is evaluated by calculating load curtailment in different parts of the network resulting from HILP outage events. The transmission network is represented as an undirected graph. Graph-theoretic techniques are used to identify islands with or without generators, potentially forming segmented grids or microgrids. This article introduces Expected Load Curtailment (ELC) as a metric to quantify the system’s resilience. The framework allows for the re-evaluation of system resilience by integrating additional generating resources to achieve desired resilience levels. Optimization is performed in the re-evaluation stage to determine the optimal placement of distributed energy resources (DERs) for enhancing resilience, i.e., minimizing ELC. Case studies on the IEEE 24-bus system illustrate the effectiveness of the proposed framework. In the broader context, this resilient IRP framework aligns with energy sustainability goals by promoting robust and resilient transmission networks, as the optimal placement of DERs for resilience enhancement not only strengthens the system’s ability to withstand and recover from disruptions but also contributes to efficient resource utilization, advancing the overarching goal of energy sustainability.

Strategic dispatch of electric buses for resilience enhancement of urban energy systems

The increasing frequency of the occurrence of high impact low probability (HILP) disruptive events has posed huge threats to the power system. Therefore, power system resilience improvement has drawn world-wide attention. Electric buses (EB) are equipped with a large capacity of batteries which could provide sufficient energy and capacity values to run islanded micro-grids (MG) for hours, with a particular focus on V2G, which will deliver system resilience during extreme operational conditions. In this paper, an innovative reciprocal transport-power system coordination scheme is proposed to simultaneously mitigate the adverse impacts on both systems. By using this economic incentive-free approach, EBs are endogenously motivated to provide resilience-oriented V2G service to the power system, while increasing the transport service survivability during HILP disruptive events. Additionally, a novel EB dispatch rescheduling method considering the trade-off of both the requirement of energy supply security and transport service fulfilment is proposed. By using this method, the essential load curtailment of local MGs can be further alleviated, while the impacts of reduced charging service on transport service can also be further mitigated. Through a series of case studies, we illustrate that EBs have significant potential to provide resilience enhancement to the urban energy system based on appropriate dispatch strategies. Meanwhile, the interests of the power system and the transport system can be well reconciled by using the proposed approach during HILP disruptive events.

Operational Reliability of Integrated Energy Systems Considering Gas Flow Dynamics and Demand-Side Flexibilities

The interdependency among the electricity, gas, heat, and cooling energy systems is ever-increasing. The flexible energy utilization patterns on the demand side and gas flow dynamics in the transmission system bring both opportunities and challenges to the reliable operation of integrated energy systems (IES). For example, if the electricity supply is interrupted, the gas system can ramp up the gas supply to the gas-fired units using linepacks. By this means, the reliability of the electricity system at this moment can be improved, while the gas system's capability of withstanding future risks may be undermined. Therefore, the operational reliability between different energy systems and time periods should be carefully balanced. This paper proposes an operational reliability evaluation framework for the IES considering flexibilities from both the demand side and transmission system. Firstly, the flexibilities of end-users and linepacks are explored based on the Energy Hub and gas flow dynamics models. Then, the reliability models of IES components are developed using the discretized-time Markov process to characterize the temporal state evolution in the operational horizon. A look-ahead contingency management scheme of the IES is then proposed to minimize the electricity and gas load curtailments. Taking account of all the possible system states, the operational reliabilities of the IES are evaluated using the time-sequential Monte Carlo simulation. Finally, the proposed method is validated by using the IEEE Reliability Test System and the practical Belgium gas transmission system.

N − 1 Security Criteria Based Integrated Deterministic and Probabilistic Framework for Composite Power System Reliability

Unpredictable variations in load demand and unanticipated component failures are progressively impacting the operation of modern power systems, making system evaluation more stochastic in nature. Although deterministic approaches were formerly the norm for determining system status, probabilistic approaches have greatly improved the capacity to capture the stochastic behavior characteristic of power system operations. The presented work in the paper recommends the use of probabilistic modelling approaches with deterministic approaches, highlighting their crucial function in augmenting the reliability and security of contemporary power systems to unanticipated failures. In this paper, N − 1 security criteria based reliability of the composite power system (CPS) is proposed using an integrated deterministic and probabilistic framework (D-P) considering outage of the transmission line. For the deterministic approach (DA), line overloading on available lines is determined using the static security index (SSI). For the probabilistic approach (PA), reliability indices such as expected loss of power (ELOP), expected frequency of contingency (EFOC), expected loss of load (ELOL), probability of load curtailment (PLC), and expected duration of load curtailments (EDLC) are calculated. Further, for each contingency, a performance index is determined using both approaches to assess the severity of the contingency that occurred on the power system. Based on the N − 1 security criteria based reliability analysis using an integrated D-P framework, a credible critical set of transmission lines is obtained, which can serve as important information to system operators. The proposed techniques have been tested on IEEE 24 bus reliability test system (RTS).