Multi-objective planning of micro-grid system considering renewable energy and hydrogen storage systems with demand response

Zhu, Juncheng; Jin, Yihua; Zhu, Wei; Lee, Dong-Kun; Bohlooli, Navid

doi:10.1016/j.ijhydene.2022.12.279

Cited by 18 publications

(4 citation statements)

References 29 publications

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“…It is evident that solar resources in Qinghai are superior to wind resources. Currently, commercialized hydrogen load scenarios mainly include industrial transport vehicle refueling stations (HS1L, Hydrogen Station 1 Load in Figure 2), passenger vehicle refueling stations (HS2L, Hydrogen Station 2 Load in Figure 2), chemical industry Energies 2024, 17, 2384 3 of 14 hydrogen load (CIHL, Figure 2), and electricity generation hydrogen load (EGHL, Figure 2), with typical energy characteristic curves [18][19][20]. The load characteristic data are shown in Table 1.…”

Section: Analysis Of Natural Resources and Hydrogen Energy Loadsmentioning

confidence: 99%

Optimization of Renewable Energy Hydrogen Production Systems Using Volatility Improved Multi-Objective Particle Swarm Algorithm

Wang,

Chen,

Yang

et al. 2024

Energies

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Optimizing the energy structure to effectively enhance the integration level of renewable energy is an important pathway for achieving dual carbon goals. This study utilizes an improved multi-objective particle swarm optimization algorithm based on load fluctuation rates to optimize the architecture and unit capacity of hydrogen production systems. It investigates the optimal configuration methods for the architectural model of new energy hydrogen production systems in Xining City, Qinghai Province, as well as the internal storage battery, ALK hydrogen production equipment, and PEM hydrogen production equipment, aiming at various scenarios of power sources such as wind, solar, wind–solar complementary, and wind–solar–storage complementary, as well as intermittent hydrogen production scenarios such as hydrogen stations, hydrogen metallurgy, and continuous hydrogen production scenarios such as hydrogen methanol production. The results indicate that the fluctuation of hydrogen load scenarios has a significant impact on the installed capacity and initial investment of the system. Compared with the single-channel photovoltaic hydrogen production scheme, the dual-channel hydrogen production scheme still reduces equipment capacity by 6.04% and initial investment by 6.16% in the chemical hydrogen scenario with the least load fluctuation.

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Section: Analysis Of Natural Resources and Hydrogen Energy Loadsmentioning

confidence: 99%

Optimization of Renewable Energy Hydrogen Production Systems Using Volatility Improved Multi-Objective Particle Swarm Algorithm

Wang,

Chen,

Yang

et al. 2024

Energies

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“…The optimal solution on the Pareto front set is identified using a fuzzy approach, and the augmented ε-constraint method was utilized to solve this multi-objective problem. In [29], considering the time-of-use rates of a demand response program, a multi-objective optimization approach was proposed for the cost-effective operation and environmental performance of intelligent parking lots (IPLs). It was advised to address this problem using the multi-objective grasshopper optimization technique, since such a model is related to several practical bounds.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the optimization methods produce simple upper and lower boundaries, which may delay the convergence speed to optimal solutions. To address this issue, we suggested a new method that makes use of the following mathematical framework to transform particles from predetermined upper and lower boundaries to places that are close to optimal areas: 𝑥 𝑖,𝑗 = 𝑏𝑒𝑠𝑡(𝑥 𝑗 ) + 𝜖 × (𝑟𝑎𝑛𝑑 × (𝑈𝐵 𝑗 − 𝐿𝐵 𝑗 )) × 𝑟𝑎𝑛𝑑 × 𝐿𝐵 𝑗 (29) The aforementioned equations improve the diversity of the best optimal solutions discovered thus far. This means that particles are not just moved from locality to optimal regions, but also the quality of the solution is increased by obtaining information from the best particle's neighborhood.…”

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confidence: 99%

Optimal Design of Grid-Connected Hybrid Renewable Energy System Considering Electric Vehicle Station Using Improved Multi-Objective Optimization: Techno-Economic Perspectives

Al-Sahlawi,

Ayob,

Tan

et al. 2024

Sustainability

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Electric vehicle charging stations (EVCSs) and renewable energy sources (RESs) have been widely integrated into distribution systems. Electric vehicles (EVs) offer advantages for distribution systems, such as increasing reliability and efficiency, reducing pollutant emissions, and decreasing dependence on non-endogenous resources. In addition, vehicle-to-grid (V2G) technology has made EVs a potential form of portable energy storage, alleviating the random fluctuation of renewable energy power. This paper simulates the optimal design of a photovoltaic/wind/battery hybrid energy system as a power system combined with an electric vehicle charging station (EVCS) using V2G technology in a grid-connected system. The rule-based energy management strategy (RB-EMS) is used to control and observe the proposed system power flow. A multi-objective improved arithmetic optimization algorithm (MOIAOA) concept is proposed to analyze the optimal sizing of the proposed system components by calculating the optimal values of the three conflicting objectives: grid contribution factor (GCF), levelled cost of electricity (LCOE), and energy sold to the grid (ESOLD). This research uses a collection of meteorological data such as solar radiation, temperature, and wind speed captured every ten minutes for one year for a government building in Al-Najaf Governorate, Iraq. The results indicated that the optimal configuration of the proposed system using the MOIAOA method consists of eight photovoltaic modules, two wind turbines, and thirty-three storage batteries, while the fitness value is equal to 0.1522, the LCOE is equal to 2.66 × 10−2 USD/kWh, the GCF is equal to 7.34 × 10−5 kWh, and the ESOLD is equal to 0.8409 kWh. The integration of RESs with an EV-based grid-connected system is considered the best choice for real applications, owing to their remarkable performance and techno-economic development.

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“…In [30], both frequency and voltage issues are addressed by proposing a centralised, adaptive load-shedding algorithm, leveraging phasor measurement units (PMUs). Zhu et al [31] present a distributed algorithm to flatten load profiles while minimising individual customer costs.…”

Section: Introductionmentioning

confidence: 99%

Impact of an ML-Based Demand Response Mechanism on the Electrical Distribution Network: A Case Study in Terni

Bucarelli,

Ghoreishi,

Santori

et al. 2023

Electronics

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The development of smart grids requires the active participation of end users through demand response mechanisms to provide technical benefits to the distribution network and receive economic savings. Integrating advanced machine learning tools makes it possible to optimise the network and manage the mechanism to maximise the benefits. This paper proceeds by forecasting consumption for the next 24 h using a recurrent neural network and by processing these data using a reinforcement learning-based optimisation model to identify the best demand response policy. The model is tested in a real environment: a portion of the Terni electrical distribution network. Several scenarios were identified, considering users’ participation at different levels and limiting the potential with various constraints.

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Multi-objective planning of micro-grid system considering renewable energy and hydrogen storage systems with demand response

Cited by 18 publications

References 29 publications

Optimization of Renewable Energy Hydrogen Production Systems Using Volatility Improved Multi-Objective Particle Swarm Algorithm

Optimization of Renewable Energy Hydrogen Production Systems Using Volatility Improved Multi-Objective Particle Swarm Algorithm

Optimal Design of Grid-Connected Hybrid Renewable Energy System Considering Electric Vehicle Station Using Improved Multi-Objective Optimization: Techno-Economic Perspectives

Impact of an ML-Based Demand Response Mechanism on the Electrical Distribution Network: A Case Study in Terni

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