Penetration Rate Of Renewable Energy Research Articles

Optimization method for capacity of BESS considering charge‐discharge cycle and renewable energy penetration rate

AbstractIn order to achieve the “carbon peaking and carbon neutrality” goals, we must vigorously develop renewable energy power generation. As the penetration of renewables progressively escalates, the corresponding demand for battery energy storage systems (BESS) within the power grid rises concomitantly. This paper presents an innovative optimization approach for configuring BESS, taking into account the incremental variations in renewable energy penetration levels and BESS charge‐discharge cycles. Employing incremental analytical techniques and pivotal metrics such as capacity elasticity, the proposed method determines the optimal penetration rate and corresponding BESS capacity outcomes for deploying energy storage systems. An example analysis of a rural power distribution benchmark is carried out by using the method in this paper, which proves the effectiveness of the method in this paper. This methodology was substantiated through its application to a case study of a rural power distribution benchmark, thereby validating its efficacy. Furthermore, it was compared with the particle swarm optimization, providing a comparative assessment of their relative performance.

Optimal scheduling of flexible grid-side resources and auxiliary service cost allocation model considering renewable-energy penetration

Abstract Renewable energy has penetrated power grid enterprises on a large scale. Due to the intermittency and volatility of renewable energy generation, it is necessary to build new flexible grid-side resources to ensure the safe and stable operation of the power grid, which will cause great pressure on cost allocation for power grid enterprises. This article considers four types of flexible grid-side resources and constructs a dual-level configuration optimization model for flexible grid-side resources under the penetration of renewable energy. Based on the configuration results, the cost scale of flexible grid-side resources is estimated and an improved ancillary service cost allocation model based on the Shapley value method is proposed to smooth the allocation of ancillary service costs in the cost of flexible grid-side resources between the two main bodies of renewable energy and load. The calculation results show that, when the penetration rate of renewable-energy power is 30% and 35%, respectively, the cost of flexible grid-side resources is 9.606 billion yuan and 21.518 billion yuan, respectively. The proportion of ancillary service costs allocated to load is relatively high—about five times that of the ancillary service costs allocated to renewable energy—and the higher the penetration rate of renewable energy, the higher the proportion of ancillary service costs allocated to renewable energy.

Industrial Park low-carbon energy system planning framework: Heat pump based energy conjugation between industry and buildings

The accelerating urbanization, rapid industrial development, and excessive consumption of fossil fuels pose survival challenges such as energy depletion and environmental degradation for humanity. In the context of industrial park development, constructing a low-carbon energy system, increasing the proportion of renewable energy, enhancing energy-level matching, and utilizing heat pumps for deep waste heat recovery are effective approaches to reduce fossil energy consumption and alleviate environmental pressures. Addressing issues such as diverse thermal flows in industrial sites, complex electricity-heat-cooling energy demands, and unclear industrial-building energy coupling mechanisms, this paper proposes a two-stage planning framework, comprising the following five steps: (1) constructing a waste heat utilization system centered around heat pumps, (2) establishing a mechanism for matching heat sources, equipment, and thermal sinks, (3) establishing an energy conversion model and energy quality quantification system, (4) optimizing waste heat utilization path allocation, and (5) conducting a system energy flow analysis. Case studies demonstrate that the proposed system achieves optimized matching of multiple heat sources and sinks in industrial and building scenarios through thermal integration, meeting diverse energy demands in the park, including electricity, cooling, and multi-grade heat. After coupling with traditional steam pipeline networks, the annual total cost and annual steam power consumption decrease by 33% and 39% respectively, with a waste heat contribution rate of up to 26% and a renewable energy penetration rate of up to 25%, and a waste heat recovery system payback period of 6 years. By establishing an energy quality quantification system and conducting multi-objective optimization considering losses and economic costs, this paper provides Pareto frontier solutions, offering references for engineering proposals. The proposed networked waste heat recovery system is characterized by low energy consumption and high economic efficiency, effectively integrating the energy characteristics of industrial parks and demonstrating engineering applicability.

Two-stage stochastic programming for multi-objective optimization of sustainable utility systems integrating with combined heat and power units

Utility systems provide thermal energy and power for industrial processes while emitting significant amounts of carbon dioxide. This study investigates the integration of Combined Heat and Power (CHP) units and renewable energy sources into utility systems in the context of diverse energy demands and low-carbon operations. A two-stage stochastic programming model (TSSP) is proposed for sustainable utility systems. The main objective of the model is to minimize the annual total cost and the other two objectives are to maximize renewable energy penetration rate and minimize grid net interaction level. The model tackles renewable energy sources’ uncertainty and evaluates environmental metrics by computing carbon emission costs. In TSSP, the first stage involves new equipment capacity planning. The second stage involves considering uncertainties related to wind speed and solar radiation. Monte Carlo simulations and probability density functions were used to generate a variety of scenarios for the optimization of operational scheduling decisions. Finally, a case study was conducted to validate the effectiveness of the proposed model. Compared to traditional deterministic optimization in utility systems, the two-stage stochastic programming of sustainable utility systems can reduce carbon emissions by 5.6 percent annually and overall costs by 8,907,733 CNY. It provides decision-makers with a range of reference options by balancing multiple objectives, including economic factors, renewable energy penetration rate, and grid interaction level.

Forecasting individual bids in real electricity markets through machine learning framework

With the increasing uncertainty caused by the complexity of the world’s energy environment and the increasing penetration rate of renewable energy, it is significant to estimate the future operation of power markets in advance. Forecasting individual bids in spot electricity markets is a promising new method for achieving so, but it has not been fully studied due to the difficulty of forecasting a bid function. The idealization of existing optimization-based models decreases their practical effects in real markets. Thus, we propose a scalable forecasting framework that incorporates several customized state-of-art machine learning methods according to the characteristics of the bidding data. First, several low-rank approximation algorithms are customized to encode the high-dimensional bidding curves into low-dimensional feature spaces and reconstruct them from the predicted feature space. Second, a transformer-based multidimensional time series prediction algorithm is proposed to predict the bidding feature based on both related factors and historical bidding records. To appropriately evaluate the performances of the forecasting methods, we introduce a dynamic criterion based on the economic implications of bids. The comprehensive framework is tested based on actual market data from the Australian national electricity market, and in the empirical example, the feasibility and effectiveness of the proposed framework are demonstrated.

Influence of the DC frequency limit controller on the frequency characteristics of the multi-area asynchronous interconnected power grid with renewable energy integration

To systematically analyze the impact of the DC frequency limit controller (FLC) configuration scheme on the frequency characteristics of asynchronous interconnected power grids with renewable energy integration, a comprehensive frequency analysis model for three-area asynchronous interconnected power grids including the FLC is proposed. The model is based on the SFR model considering renewable energy integration and includes a simplified model of the DC FLC. A rigorous validation of the rationality and stability of the model is achieved through detailed mathematical calculations and pole-zero analysis. Taking the local power grids as an example, the established model is used to study the difference between single- and double-sided configuration of the DC FLC, the relationship between the increase in the renewable energy penetration rate and the frequency characteristics of each regional power grid, and the influence of DC FLC configuration on the maximum acceptance ratio of renewable energy. Through data comparison and theoretical analysis, the influence of FLC configuration on the frequency characteristics of each region of the asynchronous power grid under renewable energy integration is obtained. The results show that the configuration of a bilateral FLC on the DC line has more advantages in alleviating the complementary power disturbance at both ends and obvious limitations on the non-complementary power disturbance at both ends. Compared with the configuration of the bilateral FLC, the configuration of a unilateral FLC on the weak grid has a larger proportion of renewable energy acceptance. When the DC FLC power transmission capacity is sufficient, the weak grid and the adjacent two asynchronous grid DC tie lines are configured with a single-sided FLC, and the weak grid is theoretically connected to the renewable energy penetration rate of up to 100%.

Coordinated planning of soft open point and energy store system in active Distribution networks under source-load imbalance

With the increasing penetration rate of renewable energy, the uncertainty of its output has exacerbated power imbalances among different distribution networks. In this paper, we propose collaborative planning of soft open points and energy storage systems to balance a distribution network with source-load imbalance, aiming to improve the economy and reliability of the distribution network. Firstly, considering that distributed generation has uncertainty, the K-means is used to construct a typical day scenario, on the basis of which a collaborative planning model is established with the objective function of minimizing the annual integrated cost. Then, the original nonlinear nonconvex model is transformed into a mixed-integer second-order conical planning model by Big-M and second-order conical relaxation, and a two-stage robust optimization is used to seek the planning solution with the lowest operational cost under the worst-case scenarios, aiming to enhance the robustness of the planning model. Finally, the feasibility of the proposed planning model is verified on two source-load unbalanced IEEE 33-node active distribution network arithmetic cases.

Methods for Coordinating Optimization of Urban Building Clusters and District Energy Systems

In the face of increasingly severe global climate change, achieving zero-carbon development goals has gradually become a consensus across various industries. Enhancing the electrification level of building energy use and increasing the proportion of renewable energy applications are primary means to achieve zero-carbon development in the construction sector, which also imposes higher demands on energy system planning and operation. This study focuses on urban building clusters and district energy systems, proposing coordinated optimization methods for energy supply and demand. On the demand side, strategies such as utilizing energy storage from electric vehicles are applied to enhance the flexibility of building energy use, along with methods to improve building load leveling rates and increase renewable energy penetration rates. On the supply side, a dual-layer planning method is proposed for the optimal configuration and operation of district energy systems considering the construction of shared energy storage stations. Results indicate that the optimization methods for urban building clusters significantly improve the flexibility of building energy use, and different functional compositions of building clusters can enhance load leveling and renewable energy penetration rates to a certain extent. The dual-layer optimization method for district energy systems can further exploit the potential of building energy flexibility, thereby achieving a balance between economic and environmental benefits.

Design and implementation of controlled load damping factor controller in series connection for power systems with high penetration of renewable energy

As sustainable natural sources such as wind and solar are increasingly integrated into power grids, conventional fossil fuel-based generating units will be gradually replaced, resulting in enormous pressure on frequency regulation of the novel power systems. This paper proposes a method for implementing a cascaded load-damping factor controller based on the controlled load-damping factor controller. Series-type voltage regulators, namely, solid-state transformers or dynamic voltage restorers, can regulate the voltage of the 10 kV feeder within the allowed range. Therefore real-time control of composite load power is realized, which significantly improves the primary frequency regulation capability of novel power systems with renewable energy generation(REG). The effect of actuator regulation time constant is considered, The impact of dynamic loads on the primary frequency regulation of the controller, designed based on a static load model, is analyzed, The effectiveness of the controller on the primary frequency regulation under different renewable energy penetration rates is discussed, The effectiveness of the controller in smoothing short-term power fluctuations of REG is examined. Finally, the effectiveness of the series-connected controlled load-damping factor controller in primary frequency regulation is verified on the RTDS simulation experimental platform. The proposed control system is characterized by a simple structure, easy implementation, and convenient maintenance, which can realize the coordination and unification of reactive-voltage and active-frequency control of the novel power system in the 110 kV customer substation and is beneficial to practical application in engineering.

Multi-Scheme Optimal Operation of Pumped Storage Wind–Solar–Thermal Generation System Based on Tolerable Energy Abandonment

In multi-energy complementary power generation systems, the complete consumption of wind and photovoltaic resources often requires more costs, and tolerable energy abandonment can bring about the more reasonable optimization of operation schemes. This paper presents a scheduling model for a combined power generation system that incorporates pumped storage, wind, solar, and fire energy sources. Through a comparison of schemes, the energy regulation function of the pumped storage power station was verified and analyzed. The CPLEX solver and MOPSO algorithm were employed to solve the daily output of a pumped storage power station in the Gansu region under various scenarios. The incorporation of pumped storage power plants has the potential to provide many benefits, including a reduction in operating expenses by about CNY 1.1163 million, a decrease in carbon emissions by 491.24 t, an enhancement in the stability of thermal power by 2.39%, and an improvement in the combined system capability to absorb additional energy. The correlation between the indicators of the combined system and the penetration rate of renewable energy is non-linearly influenced by changes in the power capacity configuration. Ultimately, the multi-objective optimization computation yields the ideal operational scheme for each power source, taking into account a tolerable energy abandonment mode.

Multi-scenario renewable energy absorption capacity assessment method based on the attention-enhanced time convolutional network

As the penetration rate of renewable energy in modern power grids continues to increase, the assessment of renewable energy absorption capacity plays an increasingly important role in the planning and operation of power and energy systems. However, traditional methods for assessing renewable energy absorption capacity rely on complex mathematical modeling, resulting in low assessment efficiency. Assessment in a single scenario determined by the source-load curve is difficult because it fails to reflect the random fluctuation characteristics of the source-load, resulting in inaccurate assessment results. To address and solve the above challenges, this paper proposes a multi-scenario renewable energy absorption capacity assessment method based on an attention-enhanced time convolutional network (ATCN). First, a source-load scene set is generated based on a generative adversarial network (GAN) to accurately characterize the uncertainty on both sides of the source and load. Then, the dependence of historical time series information in multiple scenarios is fully mined using the attention mechanism and temporal convolution network (TCN). Finally, simulation and experimental verification are carried out using a provincial power grid located in southwest China. The results show that the method proposed in this article has higher evaluation accuracy and speed than the traditional model.

Optimization of wind-solar hybrid microgrids using swarm intelligence algorithms

The study explores the enhancement of wind-solar hybrid microgrids via the use of Swarm Intelligence Algorithms (SIAs). It assesses the efficacy of these algorithms in efficiently managing renewable energy sources, load demands, and battery storage inside the microgrid system. An examination of actual data highlights the influence of environmental elements on the production of electricity, as seen by the diverse wind speeds resulting in power outputs that range from 15 kW at 4 m/s to 30 kW at 7 m/s. This underscores the clear and direct relationship between wind speed and the amount of power created. Likewise, solar irradiance levels demonstrate oscillations ranging from 500 W/m² to 800 W/m², therefore yielding power outputs that include a range of 15 kW to 24 kW, so illuminating the profound impact of solar irradiance on energy capture. The dynamic energy consumption patterns are exposed by the varying load demands, whereby the demand levels oscillate between 20 kW and 28 kW. This highlights the crucial significance of demand variability in determining energy needs. In addition, the data on battery storage reveals a range of charge levels, ranging from 25 kWh to 40 kWh, which underscores its pivotal function in the equilibrium of energy supply and consumption. When evaluating SIAs, it becomes evident that Particle Swarm Optimization (PSO) surpasses both Ant Colony Optimization (ACO) and Genetic Algorithms (GA) in obtaining an impressive 80% renewable energy penetration rate. PSO effectively reduces operating costs by 15%, demonstrating its exceptional proficiency in optimizing microgrid operations. This study provides valuable insights into the intricate interplay among environmental conditions, load demands, battery storage, and algorithmic optimization in wind-solar hybrid microgrids.

Joint operation of mobile battery, power system, and transportation system for improving the renewable energy penetration rate

Future energy system will feature in a high-share of renewable energies (REs), which poses huge challenges to obtain full utilization of renewable power generation. To solve the problem, this paper presents a joint-operation two-stage mixed integer linear programming model to coordinate the power system and train transportation system by optimizing the logistics of mobile batteries and real-time charge/discharge in cities. The full/empty batteries are transported through the train transportation system between the load side and the renewable energy station, which improves renewable energy penetration, economics, and mobilities. The first-stage model deals with the preliminary logistic optimization of batteries including routes, time and capacities of transportation, and the second-stage model optimizes real-time schedule of charge/discharge of batteries. The aim of the proposed model is to minimize the transportation cost, maximize the utilization rate of renewable energy in an energy system and smooth the daily load curve. The proposed model considers technical constraints such as railway transportation capacity, load demand satisfaction and renewable energy consumption in the power system. The optimal logistics plan and real-time charging and discharging plan can be obtained for both full and empty battery transportation. The validity of the method is verified with real data from Northeast China and Northern China, including railway routes, renewable energy output, and load profiles. The results show that the renewable energy penetration rate increases from 84.4% to 95.3% in Northeast China and from 42.7% to 78.2% in Northern China under the 2050 scenario with the proposed method. The peak-to-valley difference in the daily load curve decreases by 84% and 100% in Northeast China and Northern China, respectively. Meanwhile, the case study shows that the costs of battery transportation decrease from 0.398 CNY/kWh and 0.377 CNY/kWh to 0.252 CNY/kWh and 0.254 CNY/kWh when energy density of battery increases from 0.170 kWh/kg to 0.250 kWh/kg. This indicates that mobile energy storage has great economics. Moreover, renewable energy penetration rate is increased and peak-to-valley difference of daily load curve is decreased with no additional transmission lines.

Study of Key Parameters and Uncertainties Based on Integrated Energy Systems Coupled with Renewable Energy Sources

The extensive research and application of integrated energy systems (IES) coupled with renewable energy sources have played a pivotal role in alleviating the problems of fossil energy shortage and promoting sustainability to a certain extent. However, the uncertainty of photovoltaic (PV) and wind power in IES increases the difficulty of maintaining stable system operation, posing a challenge to long-term sustainability. In addition, the capacity configuration of each device in IES and the operation strategy under different conditions will also significantly impact the operation cost and expected results of the system, influencing its overall sustainability. To address the above problems, this paper establishes an optimization model based on linear programming to optimize the equipment capacity and operation strategy of IES coupled with PV and wind power with the minimum total annual cost as the objective function, thereby promoting economic sustainability. Moreover, an integrated assessment framework, including economic, energy efficiency, and environmental aspects, is constructed to provide a comprehensive assessment of the operation of IES, ensuring a holistic view of sustainability. Finally, taking the IES of an industrial park in Xi’an, China, as the specific case, sensitivity analysis is used to explore the impact of a variety of critical parameters on the equipment capacity and operating strategy. Additionally, the Monte Carlo method is used to explore the impact of source-load uncertainty on the performance of the IES. The results show that the facilitating or constraining relationship between renewable energy access and the cascading utilization of combined heat and power generation (CHP) energy depends on the relative magnitude of the user load thermoelectric ratio to the prime mover thermoelectric ratio. To cope with the negative impact of source-load uncertainty on the stable operation of the IES, the capacities of the electric chiller and absorption chiller should be increased by 4.0% and 5.8%, respectively. It is worth noting that the increase in the penetration rate of renewable energy has not changed the system’s dependence on the grid.

Multi-objective optimization of multi-energy complementary system based on cascade utilization of heat storage

A multi-energy complementary system driven by solar energy and central grid is proposed to supply electricity and cooling/heating, in which a dual-tank thermal storage system is integrated to achieve cascaded solar heat energy utilization. The system integrates parabolic trough solar collectors, high-temperature and low-temperature thermal storage tanks, and an organic Rankine cycle to form an operational loop. The low-temperature thermal storage tank drives an absorption heat pump independently. Excess photovoltaic power drives the ground source heat pump for heating/cooling. Three indicators are defined and compared to show the renewable energy penetration rate in the complementary system. A multi-objective optimization model considering economic performance and renewable energy rate is constructed to optimize the system configurations. Three sets of objective functions are compared through the optimization Pareto frontiers. Then, the TOPSIS method is employed to select the optimum configuration through setting different weights of optimization objectives. Compared with the reference system, the proposed multi-energy complementary system improves the energy, economic and environmental benefits by 59.21%, 24.27% and 11.31%, respectively. Through a variety of equipment (PV, ORC, AHP, TES), solar energy supplies 86.2% and 72.2% of total electricity and heat loads, respectively.

Game and multi-objective optimization of configurations for multiple distributed energy systems considering building users’ demands and satisfaction degrees

Sustainable building energy supply is crucial to the sustainable development of society, and replacing the separation production energy system with a hybrid renewable energy distributed energy system (DES) can help achieve this goal. However, there are challenges to the application of such building DES, such as collaboration between multiple DESs, conflicts between DESs and users, and user energy management/evaluation. While existing works rarely consider them simultaneously. Therefore, this work presents a two-stage collaborative optimization model to solve these issues. The first-stage optimizes the structures and capacities of DESs and the electricity and heat interchanges between DESs to maximize energy, economic, and emission performances. Based on user energy consumption elasticity, the second-stage evaluates DES revenue and user energy consumption utility/satisfaction to perform a multi-leader and multi-follower game. The interactive game determines energy prices and manages the hourly building user demands. A case study shows the validation of the proposed method. The results indicate that from the initial stage to reaching game equilibrium, the revenue of DESs decreases by approximately 11.2 % due to the declined energy prices, which increase users' hourly loads. Then, users’ utility can be increased by 7.0 % due to satisfaction improvement. Moreover, the renewable energy penetration rate in DESs is increased by 7.1 %, and the relative energy, economic, and emission performances (the baseline is traditional separation production system) of DESs are increased by 5.9 %, 0.5 %, and 4.5 %, respectively. The proposed framework helps to design/manage clean and efficient building energy systems and formulate reasonable energy prices, favoring DES development and carbon neutrality goals.

Optimal Configuration of Power/Thermal Energy Storage for a Park-Integrated Energy System Considering Flexible Load

The park-integrated energy system can achieve the optimal allocation, dispatch, and management of energy by integrating various energy resources and intelligent control and monitoring. Flexible load participation in scheduling can reduce peak and valley load, optimize load curves, further improve energy utilization efficiency, and reduce system costs. Based on this, firstly, a flexible power-load model is established considering the translatable load, transferable load, and reducible load; and a thermal flexible load model is established based on the fuzziness of user perception of temperature in this study; then, the mixed integer linear programming method is adopted, and the sum of the carbon transaction cost, operation and maintenance cost, compensation cost, power purchase cost, gas purchase cost, wind and light abandonment penalty cost and investment cost of the system is minimized as the objective function, and the configuration of the integrated energy system is optimized, and the optimal capacity of each equipment and the output of each period are obtained. Finally, taking an industrial park in Liaoning Province of China as an example, the analysis is carried out. The example results show that by scheduling the flexible electrical load and flexibly adjusting the indoor temperature, renewable energy consumption can be promoted, and electricity load and heat-load curves can be optimized to increase the installed capacity of wind turbines, reduce the capacity of gas turbines, batteries, and heat-storage tanks, improve system economy, and improve the penetration rate of renewable energy.

Planning shared energy storage systems for the spatio-temporal coordination of multi-site renewable energy sources on the power generation side

The application prospects of shared energy storage services have gained widespread recognition due to the increasing use of renewable energy sources. However, the decision-making process for connecting different renewable energy generators and determining the appropriate size of the shared energy storage capacity becomes a complex and interrelated problem when considering the multi-site spatio-temporal characteristics. This paper presents an optimal planning and operation architecture for multi-site renewable energy generators that share an energy storage system on the generation side. Furthermore, an economic-environmental model is proposed to minimize the costs associated with the energy system infrastructure while maintaining a high penetration rate of renewable energy. The centralized multi-objective model allows renewable energy generators to make cost-optimal planning decisions for connecting to the shared energy storage station, while also optimizing the size of the storage capacity to maximize renewable energy generation and minimize costs. The Non-dominated Sorting Genetic Algorithm-II is employed in a centralized manner to solve the multi-objective nonlinear model. Numerical experiments are conducted to demonstrate the economic and environmental benefits of the proposed system. Therefore, this method is highly recommended for implementation on the generation side.

Optimal planning of energy storage system under the business model of cloud energy storage considering system inertia support and the electricity-heat coordination

As the penetration rate of renewable energy increases in the electric power system, the issues of renewable power curtailment and system inertia shortage become more severe. Innovative solutions such as Cloud Energy Storage (CES) can be employed to address this challenge. However, the energy storage resources aggregated by the traditional CES business model mainly concentrate on Electrical Energy Storage (EES), which is still limited and expensive. It necessitates the exploration of new approaches to enhance the flexibility and cost-effectiveness of energy storage utilization, in which using District Heating System (DHS) as an equivalent energy storage resource of the power system is an effective method. Therefore, this paper proposes an optimal planning strategy of energy storage system under the CES model considering inertia support and electricity-heat coordination. Firstly, the system components and business model of the CES are described, and the framework of energy storage planning problem from the perspective of CES operator is formulated. Then the evaluation methods of energy storage utilization demand from CES users are proposed, including the evaluation of the renewable power curtailment, system minimum inertia requirement, and the equivalent energy storage ability of DHS. Based on this evaluation results, a bi-layer optimal energy storage planning model for the CES operator is established, where the upper-layer model determines the installed capacity of lithium (Li-ion) battery station and the lower-layer model determines the optimal schedules of the CES system. The numerical tests based on the operation profile of a typical city in China are carried out to demonstrate the effectiveness of the proposed method. The simulation result shows that the annual profit of the CES system can be improved by 15.26% after installing the energy storage system whose capacity is determined by the proposed method.

Integrating heat pumps into district heating systems: A multi-criteria decision analysis framework incorporating heat density and renewable energy mapping

The implementation of district heating (DH) projects necessitates a thorough and accurate evaluation and planning process. The availability of energy resources is often the key determinant in identifying the suitability of a specific location for district heating applications. Also, it is crucial to consider the other factors during the planning stage: the environment, economics, and technical parameters. This research aims to plan and implement a DH site selection model based on a geographic information system (GIS). This model employs Boolean and fuzzy toolboxes to analyze various layers, followed by the analytic hierarchy process (AHP) to identify the most appropriate locations for district heating in Gaziantep, Turkey. A mixed integer linear programming (MILP) approach is utilized to model proposed energy systems to achieve optimal operation and planning. The study's primary objective is to minimize the levelized cost of energy (LCOE), which encompasses initial costs, operations, maintenance, replacement expenses, and energy demand while also meeting the energy demand. A hybrid energy system that includes solar thermal/photovoltaic (ST, PV) energy, wind turbine (WT), and heat pump (HP) is considered, and its LCOE of optimal size is compared to other scenarios using HOMER simulation software. The results indicate that 270 km2 of the study area is suitable for DH implementation, with 83 km2 being highly suitable. Among the various scenarios, the hybrid energy system with a renewable energy penetration rate of approximately 78% achieves the best LCOE of 0.0442. The study concludes by providing a map of the potential for district heating and a discussion of the strengths and drawbacks of each scenario.