Microgrid Planning Including Renewables Considering Optimum Compressed Air Energy Storage Capacity Determination Using HANN-MDA Method

Subject Areas : International Journal of Smart Electrical Engineering

Seyedamin Saeed ^{1 *} , Tahere Daemi ² , Zohreh Beheshtipour ³

1 - Department of Electrical Engineering, Yazd Branch, Islamic Azad University, Yazd, Iran
2 - Faculty of Electrical Engineering, Yazd Branch, Islamic Azad University, Yazd, Iran
3 - Department of Electrical Engineering, Yazd Branch, Islamic Azad University, Yazd, Iran

Received: 2024-01-18 Accepted : 2024-03-10 Published : 2024-03-22

Keywords: Microgrids, Compressed Air Energy Storage, Hybrid Artificial Neural Network, Modified Dragonfly Algorithm.,

Abstract :

Microgrids, with their ability to integrate renewable energy sources, play a crucial role in achieving sustainable and resilient energy systems. Effective planning and optimization of microgrids, particularly considering the inclusion of compressed air energy storage (CAES) systems, are essential for maximizing their benefits. This study proposes a novel approach, the Hybrid Artificial Neural Network-Modified Dragonfly Algorithm (HANN-MDA), for determining the optimum capacity of CAES in microgrid planning. The HANN-MDA method combines the learning capabilities of artificial neural networks with the optimization power of the modified dragonfly algorithm. The proposed method aims to minimize the overall cost of microgrid operation while considering the integration of renewable energy sources and the storage capabilities of CAES. Simulation results demonstrate the effectiveness of the HANN-MDA method in accurately determining the optimal CAES capacity, leading to improved microgrid performance and cost savings. The findings highlight the importance of considering CAES in microgrid planning and the potential of the HANN-MDA method for achieving efficient and economically viable microgrid designs.

Full-Text:



237                          International Journal of Smart Electrical Engineering, Vol.13, No.1, Winter 2024                             ISSN: 2251-9246        

EISSN: 2345-6221

 

pp. 9:24

 

Microgrid Planning Including Renewables Considering Optimum Compressed Air Energy Storage Capacity Determination Using HANN-MDA Method

Mohammad Mirshams1, Seyed Amin Saeed2*, Tahere Daemi3, Zohreh Beheshtipour4

Department of Electrical Engineering, Yazd Branch, Islamic Azad University, Yazd, Iran. [e-mail] 

Abstract

Microgrids, with their ability to integrate renewable energy sources, play a crucial role in achieving sustainable and resilient energy systems. Effective planning and optimization of microgrids, particularly considering the inclusion of compressed air energy storage (CAES) systems, are essential for maximizing their benefits. This study proposes a novel approach, the Hybrid Artificial Neural Network-Modified Dragonfly Algorithm (HANN-MDA), for determining the optimum capacity of CAES in microgrid planning. The HANN-MDA method combines the learning capabilities of artificial neural networks with the optimization power of the modified dragonfly algorithm. The proposed method aims to minimize the overall cost of microgrid operation while considering the integration of renewable energy sources and the storage capabilities of CAES. Simulation results demonstrate the effectiveness of the HANN-MDA method in accurately determining the optimal CAES capacity, leading to improved microgrid performance and cost savings. The findings highlight the importance of considering CAES in microgrid planning and the potential of the HANN-MDA method for achieving efficient and economically viable microgrid designs.

Keywords: Microgrids, Compressed Air Energy Storage, Hybrid Artificial Neural Network, Modified Dragonfly Algorithm 

Article history: Received 2024/01/18; Revised 2024/03/02; Accepted 2024/03/10, Article Type: Research paper

© 2024 IAUCTB-IJSEE Science. All rights reserved

  https://doi.org/10.82234/ijsee.2024.1074786

1.       Introduction

 

The increasing integration of renewable energy sources into power systems has spurred the need for advanced planning strategies to enhance grid resilience, reliability, and sustainability. Microgrids (MGs), as localized and self-sufficient energy systems, have emerged as a promising solution to address these challenges [1]. This research focuses on the intricate domain of microgrid planning, emphasizing the critical role of Compressed Air Energy Storage (CAES) systems in mitigating uncertainties associated with renewable energy sources, ultimately aiming for cost minimization. In recent years, the global energy landscape has witnessed a transformative shift towards cleaner and more sustainable alternatives as shown in Figure (1). The escalating concerns about climate change, coupled with advancements in renewable energy technologies, have accelerated the adoption of solar, wind, and other clean energy sources. However, the inherent intermittency and variability of renewables pose significant challenges to the stability and reliability of power grids. MGs which characterized by their decentralized nature and ability to operate independently or in conjunction with the main grid, offer a viable solution to integrate renewables seamlessly [2].

 

 

Fig. 1.       a typical microgrid equipped with CAES [2]

Microgrid planning involves the optimal allocation of resources, considering both the demand and supply sides. The integration of renewable energy sources, such as solar and wind, adds complexity due to their fluctuating nature [3]. Accurate forecasting and management of these uncertainties are paramount for ensuring a stable and efficient microgrid operation. This research aims to delve into the nuances of microgrid planning, focusing on effective strategies to accommodate renewable energy sources and mitigate their inherent variability. CAES systems have emerged as a promising technology to address the intermittency challenges associated with renewables [4]. By storing excess energy during periods of abundance and releasing it during high demand, CAES enhances grid stability and reliability. However, determining the optimum capacity of CAES is a complex task, especially when considering uncertainties associated with renewable energy generation. This research aims to develop a comprehensive framework for microgrid planning that incorporates the optimal determination of CAES capacity, taking into account the uncertainties inherent in renewable energy sources. Microgrid planning is a multidimensional optimization problem that requires careful consideration of various factors, including energy demand, generation capacity, storage capabilities, and economic constraints. The dynamic nature of renewable energy sources adds an additional layer of complexity to this optimization process. Traditional approaches may fall short in providing effective solutions that balance these diverse and dynamic parameters. This research seeks to address this gap by proposing an advanced optimization model that integrates the uncertainties associated with renewables, with a specific focus on determining the optimum capacity of CAES [5-7]. Optimum scheduling in an economic MG involves determining the optimal operation of the MG to minimize its operating costs while meeting the energy demand of its local consumers. One key factor in optimizing the operation of a MG is the determination of the optimum capacity of its energy storage systems. It could be determined based on several factors such as the energy demand profile of the local consumers, the availability and cost of the distributed generation sources and the price of electricity in the upstream network [8]. The energy storage system capacity needs to be large enough to store excess energy generated from renewable sources during times when the demand for electricity is low and supply energy during times when the demand for electricity is high. To determine the optimum capacity of energy storage systems, an optimization model can be developed that considers various factors such as the energy demand profile, the availability and cost of the distributed generation sources, and the price of electricity in the upstream network. The objective of the optimization model is to minimize the operating costs of the MG while meeting the energy demand of its local consumers. Once the optimum capacity of energy storage systems is determined, the MG can be operated to minimize its operating costs while meeting the energy demand of its local consumers [9].

A.       Literature Review and Research Gap

This paper introduces a novel optimization framework that focuses on scheduling Distributed Energy Resources (DERs) within MGs, with particular emphasis on determining the optimal energy storage capacity. The proposed methodology takes into account economic factors, technical considerations, and the dynamic behavior of DERs to achieve improved economic efficiency and reliability in MG operation [10]. In a related study, the performance of various optimization algorithms, including genetic algorithm, particle swarm optimization, and dynamic programming, is compared for scheduling DERs in MGs. The results highlight the effectiveness and computational efficiency of these different techniques. Another review paper provides an overview of economic dispatch strategies in MG operation. The authors discuss different objective functions, dispatch strategies, and their impact on optimizing economic performance, considering the integration of DERs and Energy Storage Systems (ESS) [11]. Furthermore, a methodology is presented in a separate paper for determining the optimum energy storage capacity in MG systems. This approach considers economic factors, technical considerations, and the dynamic behavior of DERs to determine the most suitable energy storage capacity [12]. A different study investigates the integration of renewable energy sources and energy storage systems in MGs. The authors discuss the benefits, challenges, and optimization strategies for scheduling DERs while considering the determination of energy storage capacity [13]. A review paper explores various methodologies for sizing energy storage systems in MGs, comparing deterministic and probabilistic approaches. The economic and technical considerations involved in energy storage sizing are highlighted [14]. In addition, a paper proposes a stochastic economic dispatch model for MGs that takes into account the uncertainty of DERs. The authors analyze the effect of uncertainty on MG operation and demonstrate improved economic performance [15]. Another study presents a multi-objective optimization approach for economic dispatch of DERs in MGs, considering cost minimization, emission reduction, and reliability enhancement as conflicting objectives [16]. To tackle optimal scheduling and energy storage capacity determination, a hybrid algorithm combining particle swarm optimization and artificial neural networks is proposed in a paper. This approach demonstrates improved convergence and accuracy [17]. Load forecasting techniques and their application in MG scheduling are reviewed in another paper. Accurate load forecasting is crucial for optimal scheduling while considering energy storage capacity determination [18]. A robust scheduling strategy is proposed in a paper for DERs in MGs, considering probabilistic energy storage sizing. The approach accounts for uncertainties in DERs and optimizes MG operation under various scenarios [19]. Another study focuses on energy storage sizing for enhancing MG resilience. An optimization framework is proposed that considers the capacity and performance of energy storage systems to ensure reliable and resilient MG operation [20]. A real-time scheduling approach for DERs in economic MGs is presented in a paper. The authors consider the dynamic behavior of DERs and optimize their dispatch in response to changing grid conditions [21]. Optimal power flow modeling for MGs, with a focus on determining the optimum energy storage capacity, is proposed in another paper. The authors demonstrate improved economic efficiency and reliable operation of MGs using this model [22]. The integration of demand response management with MG scheduling, considering energy storage capacity determination, is investigated in a study. The authors analyze the impact of demand response on economic performance and grid stability [23]. A comprehensive framework for optimal scheduling of DERs with energy storage in economic MGs is proposed in a paper. Various factors such as cost, reliability, and environmental impact are considered to optimize MG operation [24]. The effect of energy storage capacity on MG stability and resilience is analyzed in a study. The authors examine the relationship between storage capacity, DERs, and grid stability under different operating conditions [25]. An optimal allocation method for energy storage systems in MGs is addressed in a paper. The authors propose a method that considers the location, capacity, and cost of energy storage to optimize MG performance [26]. The impact of DER and ESS integration on MG operation costs is analyzed in a study. The authors quantify the economic benefits and cost savings achieved through optimized scheduling and energy storage capacity determination [27]. Finally, authors in [28] paper investigates the enhancement of renewable energy utilization in economic MGs through optimal scheduling. Scheduling strategies are proposed to maximize the utilization of renewable energy sources while considering energy storage capacity determination.

While the existing literature provides valuable insights into microgrid planning, renewable energy integration, and the role of CAES, there is a noticeable gap in research specifically addressing the dynamic uncertainties associated with renewables in the context of CAES integration within microgrids. The need for a comprehensive optimization model that considers these uncertainties and determines the optimal CAES capacity is evident, pointing to the motivation for the current research.

B.       Research Objectives

This research holds significant implications for the advancement of microgrid planning strategies in the context of increasing renewable energy integration. The developed optimization model, incorporating CAES capacity determination and addressing uncertainties, is expected to contribute to more resilient and cost-effective microgrid designs. By optimizing the deployment of CAES, the research aims to enhance the overall efficiency of microgrids and accelerate their adoption as a sustainable solution for decentralized energy generation. Therefore, the primary objectives of this research are as follows:

- Develop a comprehensive understanding of microgrid planning and the challenges posed by the integration of renewable energy sources.

- Investigate the role of CAES in mitigating uncertainties associated with renewables and enhancing microgrid stability.

- Propose an optimization model that considers the dynamic nature of renewable energy generation and determines the optimum capacity of CAES for cost minimization.

- Evaluate the proposed model through simulations and case studies to demonstrate its effectiveness in real-world microgrid scenarios.

C.       Research Structure

The remainder of this research will be organized as follows: Section 2 represents the methodology, so that as an in-depth exploration of the proposed optimization model, detailing the incorporation of renewable uncertainties and the determination of optimal CAES capacity are investigated. A brief review on optimization algorithm is represented in Section 3. The results and discussion are presented in Section 4 as presentation and analysis of simulation results, demonstrating the effectiveness of the proposed model in diverse microgrid scenarios. The discussions are represented in Section 5. Finally, a summary of key findings, implications, and avenues for future research in the field of microgrid planning and renewable energy integration are expressed in Section 6 as the conclusion.

2.       Methodology

Optimizing a microgrid with diverse energy resources, including renewable sources, microturbines, and a CAES system, involves a complex framework aimed at achieving multiple objectives. Microgrids represent a modern approach to decentralized and sustainable energy systems. The integration of renewable energy sources, such as wind turbines () and solar photovoltaics (), alongside conventional generators like microturbines () and innovative storage solutions like CAES, offers a robust and resilient energy infrastructure. The primary goal is to formulate an optimization framework that minimizes the total planning costs while ensuring reliable and environmentally conscious microgrid operation.

The framework involves several decision variables, each influencing the microgrid's operation. These include power outputs of generators , renewable power outputs , , CAES compression and discharge powers , , CAES energy level  power not served , excess power , and emissions of pollutants . The overarching objective is to minimize the total planning costs . This cost includes the generation costs of wind , solar , and microturbine  sources, the cost of energy not served  the cost of excess generation , and penalty costs associated with pollutant emissions  The objective function encapsulates economic, environmental, and reliability considerations.

A.       Power Balance Constraints

The power balance equation ensures that the total electric power generated from various sources, accounting for CAES processes and considering excess or deficit power, matches the electric load demand at each time period. Here is the refined version of the power balance equation:

(1)

This equation expresses the equilibrium between the sum of power outputs from generators (), wind turbines (), solar PV (), and CAES discharge (), minus the CAES compression (), plus excess power () and minus the power not served (), which equals the electric load demand () at each time period (). This equation forms the foundation for maintaining a stable and balanced operation of the microgrid, ensuring that the total power supplied meets the demand at all times.

B.       Renewable Power Output Constraints

These constraints limit the power output of wind turbines and solar PV, accounting for uncertainties and . The constraints ensure that the generated renewable power does not exceed the specified maximum values, considering variations due to uncertainties, and contribute to maintaining grid stability.

(2)

To account for uncertainties in renewable energy sources, the power outputs of wind turbines and solar PV are modified by uncertainty factors and . These uncertainties reflect the variations in wind and solar power generation, ensuring a more realistic representation of the renewable energy inputs in the optimization framework.

C.       CAES Energy Limits

These constraints define the allowable range for the energy stored in the CAES system. It prevents overcharging or complete depletion, ensuring the CAES system operates within its capacity limits and contributes effectively to grid stability.

	(3)
	(4)

D.       Generator Output Limits

These constraints impose upper bounds on the power output of each generator (wind turbines, solar PV, microturbines). They prevent excessive generation that could lead to operational challenges and contribute to the overall control of the microgrid.

(5)

E.       CAES Compression/Discharge Limits

These constraints regulate the power flow during CAES compression and discharge processes, ensuring they operate within the specified efficiency limits. They play a crucial role in optimizing the CAES system's performance and efficiency.

(6)

F.       Emission Calculations

These equations quantify the emissions of pollutants (, , ) based on the power output of each generator and their respective emission rates. It helps in assessing the environmental impact of the microgrid operation and guides decision-making towards cleaner energy sources.

	(7)
	(8)

G.       Non-negativity Constraints

These constraints enforce that all decision variables, including power outputs, energy levels, and emissions, remain non-negative. It reflects the physical constraints of the system, preventing unrealistic or negative values.

(9)

H.       CAES Compression Energy

This equation calculates the energy consumed during CAES compression. It considers the compression power, efficiency, and time interval, providing insights into the energy requirements for storing compressed air in the CAES system.

(10)

I.       CAES Expansion Energy

Similar to the compression energy equation, this calculates the energy released during CAES expansion. It is a key component in understanding the energy dynamics of the CAES system during discharge.

(11)

J.       CAES Compression Exergy

This equation computes the exergy (availability to do work) associated with the compressed air during CAES compression. It's a thermodynamic measure reflecting the quality of the stored energy in the CAES system.

(12)

K.       CAES Expansion Exergy

Similar to compression exergy, this equation calculates the exergy associated with the expanded air during CAES discharge. It provides insights into the thermodynamic efficiency of the CAES process.

(13)

L.       CAES Compression Exergy Efficiency

This equation defines the exergy efficiency during CAES compression. It quantifies how effectively the compression process converts input energy into exergy, offering a measure of the system's thermodynamic performance.

(14)

M.       CAES Expansion Exergy Efficiency

Parallel to compression exergy efficiency, this equation characterizes the exergy efficiency during CAES expansion. It evaluates the effectiveness of the CAES system in converting stored energy into useful work.

(15)

N.       CAES Compression Exit Temperature

This equation calculates the exit temperature of air after compression in the CAES system. It considers the compression power, efficiency, and thermodynamic properties, providing insights into the thermal aspects of the compression process.

(16)

O.       CAES Expansion Exit Temperature

Similar to compression exit temperature, this equation determines the exit temperature of air during CAES expansion. It offers valuable information on the thermal conditions at the end of the expansion process.

(17)

P.       CAES Polytropic Index Limits

These constraints restrict the polytropic index within specified bounds. The polytropic index influences the thermodynamic behavior of the compressed air, and these limits ensure it stays within a reasonable range for stable operation.

(18)

Q.       CAES Inventory Energy Limit

This equation ensures the conservation of energy in the CAES inventory, preventing energy imbalances between compression and discharge processes. It contributes to the overall energy accountability within the CAES system.

(19)

R.       CAES Compression Air Costs

This equation computes the cost associated with compressing air in the CAES system, considering the cost of air and operational parameters. It helps in evaluating the economic implications of the compression process.

(20)

S.       CAES Thermal Efficiency

This equation defines the thermal efficiency of the CAES system during discharge. It quantifies the ratio of useful work output to the energy input, providing a measure of the system's overall energy conversion efficiency.

(21)

T.       Cost of Energy Not Served

This equation calculates the cost associated with the energy not served to meet the demand. It represents the economic consequences of insufficient power generation and guides decision-making to minimize such costs.

(22)

U.       Cost of Excess Generation

Similar to energy not served, this equation computes the cost associated with excess generation beyond the demand. It reflects the economic impact of overgeneration and aids in optimizing the microgrid operation economically.

(23)

V.       Generator Emission Penalties

These constraints limit the product of pollutant emissions and penalty costs for each generator. They provide an upper bound on the economic impact of emissions from the generators, facilitating environmentally conscious decision-making.

(24)

These constraints ensure that the total economic impact of emissions from each generator, weighted by the penalty costs (), remains within acceptable bounds (). It promotes a balance between economic considerations and environmental sustainability, aligning with the overarching goal of minimizing total planning costs.

W.       Maximum Renewable Power

These constraints restrict the power output of wind turbines and solar PV to their respective maximum values, accounting for uncertainties and . They play a crucial role in preventing excessive renewable energy generation, which could lead to operational challenges and contribute to grid instability.

(25)

X.       Microturbine Limits

These constraints ensure that the power output of microturbines remains within specified minimum and maximum values. They contribute to the overall control of the microgrid and prevent operational challenges associated with microturbine power.

	(26)
	(27)

These constraints on microturbine power output serve as important operational limits, preventing underutilization or overloading of microturbine assets in the microgrid.

Y.       Demand Response

Demand response (DR) plays a pivotal role in influencing and enhancing the proposed microgrid planning framework. The integration of demand response mechanisms introduces a dynamic element that significantly impacts the optimization of microgrid operations, renewable energy integration, and the determination of CAES capacity. Here are the key effects:

DR introduces flexibility by allowing the microgrid to dynamically respond to changes in energy demand. This enhances the adaptability of the microgrid planning framework, enabling real-time adjustments to optimize energy generation, consumption, and storage. By incorporating demand response, the microgrid gains the ability to actively manage energy consumption during peak demand periods. This results in improved grid stability and reliability, reducing the risk of overloads and enhancing overall system resilience.

DR facilitates better utilization of available resources by aligning energy consumption with periods of high renewable energy generation. This optimization ensures that energy storage systems, including CAES, are utilized efficiently, minimizing waste and reducing operational costs. DR mechanisms contribute to economic benefits by allowing the microgrid to participate in demand-side management programs. This enables the microgrid to capitalize on favorable pricing conditions, reduce peak demand charges, and potentially generate revenue through demand response participation.

The effect of DR on microgrid planning is particularly pronounced when integrating renewable energy sources. DR enables the alignment of energy consumption with periods of high renewable generation, maximizing the utilization of clean energy and reducing dependence on non-renewable sources. DR allows for dynamic load balancing, enabling the microgrid to optimize the distribution of energy based on real-time demand fluctuations. This ensures efficient utilization of generation and storage resources, contributing to overall energy efficiency.

The inclusion of demand response in the microgrid planning framework helps mitigate grid congestion during peak demand periods. By encouraging load-shifting and load reduction strategies, DR reduces stress on the grid and minimizes the risk of system failures. DR enhances the microgrid's ability to withstand disruptions by actively managing energy demand. This improved energy resilience is crucial in the face of uncertainties, contributing to the overall reliability and robustness of the microgrid.

DR can influence the operation of CAES systems by aligning energy consumption with periods of low demand or high renewable energy availability. This ensures that CAES operates optimally, supporting grid stability and minimizing the need for additional energy sources. The effect of demand response extends to the integration of advanced technologies, such as smart grids and IoT devices. These technologies enhance the responsiveness of demand-side management, providing real-time data for more accurate decision-making within the microgrid planning framework.

In general, the incorporation of demand response in the microgrid planning framework brings about a multitude of positive effects, ranging from improved grid stability and economic benefits to optimized resource utilization and enhanced resilience. This dynamic interaction strengthens the overall sustainability and efficiency of microgrid operations in the face of evolving energy landscapes, as can be formulates like the following:

	(28)
	(29)

---