High Penetration Of Renewable Energy Research Articles

Multi-scenario flexibility assessment of power systems considering renewable energy output uncertainty

The widespread adoption of renewable energy sources presents a significant challenge to the flexibility of power system. To assess the flexibility of the power system in scenarios with uncertain renewable energy output, it is crucial to quantify it quantitatively. This evaluation plays a vital role in planning flexible regulatory resources and dispatching resources for both the energy source and load. This study introduces a novel flexibility assessment model tailored for power grids with high renewable energy penetration, specifically addressing uncertainty associated with wind and PV. By analyzing the impact of wind and PV uncertainty on system flexibility, the paper proposes an improved cohesive hierarchical cluster analysis method, incorporating reliability considerations based on the Davies-Bouldin classification reliability index. Additionally, the study develops models for flexibility resources and demands within high renewable energy power systems, along with quantitative assessment indicators across three key aspects. Through a structured flexibility assessment process accounting for wind and PV uncertainty, the effectiveness of the proposed approach is validated using real-world data from a renewable energy power grid in Shandong province. A set of typical renewable energy output scenarios with uncertainty is constructed using the improved hierarchical cluster analysis method. The study then analyses the impact of different wind and PV penetration rates and the proportion of energy storage units on system flexibility by the flexibility assessment model to validate the proposed method's effectiveness.

Available Transfer Capability Assessment of Multiarea Power Systems with Conditional Generative Adversarial Network

Available transfer capability (ATC) is an important measurement index to evaluate the security margin of interconnected power grids and serve as a reference for the transmission right transaction. In modern power systems, ATC is affected by the transmission network topology, renewable power output uncertainty, and load demand uncertainty. Traditional works usually model the power source-load uncertainty by using robust optimization, interval optimization, or chance-constraint optimization, which cannot fully reflect the probabilistic distribution of the daily source-load uncertainty. This paper proposes an ATC assessment methodology based on the typical stochastic scenarios of renewable output and load demand of multiarea power systems. Furthermore, the conditional generative adversarial network (CGAN) algorithm is adopted to generate and select representative scenario sets based on historical raw data, which can fully reflect the usual operating condition of a system with high renewable energy penetration. The scenario set that is fed into the ATC assessment model can fully characterize the impact of source-load uncertainty on daily ATC. Finally, the proposed method is verified by a modified three-area IEEE 9-bus system and a real-world provincial power system.

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.

Demand flexibility and cost-saving potentials via smart building energy management: Opportunities in residential space heating across the US

Leveraging demand-side flexibility resources (e.g., buildings) is a crucial and cost-effective strategy for addressing the operational and infrastructure-related challenges in power grids pursuing deep decarbonization with high renewable energy penetration. However, the demand flexibility opportunities and financial benefits for residential space heating, which are sizeable demand-side flexibility resources, through emerging building energy management solutions (i.e., smart control and phased change material (PCM) thermal storage) across the US are not fully understood. In this paper, we systematically assess the demand flexibility and cost-saving/revenue potentials in residential space heating through detailed building-level simulations for five consecutive years at a 5-min temporal resolution in 20 metro areas across the high-heating-demand regions of the US. The results show a high degree of synergy between PCM thermal storage and smart control, which enables substantial demand flexibility potential, reaching 98.5% of peak load shifting, and electricity cost-saving/revenue potential, reaching 338.3% of electricity cost reductions, for residential space heating in the US. By achieving such performance, adopting smart control and PCM thermal storage is financially viable in 50% of the tested metro areas. The results reveal that the demand flexibility and cost-saving/revenue potentials of residential space heating in the US are further enhanced by higher volatilities in electricity prices. Active PCM thermal storage has lower energy efficiency but much higher energy flexibility than passive PCM thermal storage. Based on the findings, recommendations for integrating PCM thermal storage and smart control systems within residential space heating are provided.

Voltage Optimization in Active Distribution Networks—Utilizing Analytical and Computational Approaches in High Renewable Energy Penetration Environments

This review paper synthesizes the recent advancements in voltage regulation techniques for active distribution networks (ADNs), particularly in contexts with high renewable energy source (RES) penetration, using photovoltaics (PVs) as a highlighted example. It covers a comprehensive analysis of various innovative strategies and optimization algorithms aimed at mitigating voltage fluctuations, optimizing network performance, and integrating smart technologies like smart inverters and energy storage systems (ESSs). The review highlights key developments in decentralized control algorithms, multi-objective optimization techniques, and the integration of advanced technologies such as soft open points (SOPs) to enhance grid stability and efficiency. The paper categorizes these strategies into two main types: analytical methods and computational methods. In conclusion, this review underscores the critical need for advanced analytical and computational methods in the voltage regulation of ADNs with high renewable energy penetration levels, highlighting the potential for significant improvements in grid stability and efficiency.

Performance evaluation and operation optimization of a combined heat and power plant integrated with molten salt heat storage system

Combined heat and power is an effective way to enhance the energy efficiency of thermal power plants. The solar and wind power are intermittent, and dispatchable power sources are critical to balance the supply and demand sides. To facilitate the high penetration of renewable energy, combined heat and power (CHP) plants should provide more and more peak shaving services for the power grid. The thermal energy can be stored in and released from the molten salt heat storage system (MSHSS). The integration of MSHSS can enlarge the adjustable range of the power load of CHP plants, so it is a potential way to enhance the operational flexibility of CHP plants supplying industrial steam and power. In this study, the MSHSS integrated within CHP plants are proposed, which store a part of sensible heat energy carried by extraction steam before supplying to industrial users. The heat storage capacity dominates the flexibility enhancement, and indicators to evaluate energy performances of the proposed system are defined. Moreover, models for the operation optimization of CHP plants integrated with the MSHSS are developed, and a case study of a 350 MW CHP unit is conducted. When the reheat steam is extracted to supply to industrial steam, the maximum and minimum adjustable power loads can be increased and decreased by 17.13 and 12.01 MW, respectively with the integration of the MSHSS. Moreover, during the peak, flat, valley time of reference day, the heat supply coal consumption rates are 73.94, 74.19 and 81.03 kg t−1,respectively. With the operation scheduling of CHP plant integrated with MSHSS, the MSHSS with reheat steam as heat source show the lowest coal consumption rate as 1553.79 ton/day.

Low Inertia Power System Planning Considering Frequency Quality Under High Penetration of Renewable Energy

Low Inertia Power System Planning Considering Frequency Quality Under High Penetration of Renewable Energy

A load-side resource aggregation method based on Minkowski Sum

In the context of high penetration of renewable energy in new power systems, load-side resources have good regulation potential to improve system flexibility. In order to fully exploit the regulation potential of load-side resources, the power regulation range and regulation cost of load-side resources need to be aggregated first. Based on the establishment of a unified model of the scheduling feasible region of the load-side resources, the computation of the Minkowski sum of the feasible region of the load-side resources is realized through the linear superposition of the maximally inner-approximated feasible region. The decomposition scheme of the instructions of the aggregator and the computation method of the equivalent cost function of the aggregator are proposed. The effectiveness of the proposed aggregation method is verified by arithmetic simulation.

Multi-Stage Planning of Distribution Network With High Penetration Renewable Energy Considering Reliability Index

Multi-Stage Planning of Distribution Network With High Penetration Renewable Energy Considering Reliability Index

Voltage Stability Constrained Economic Dispatch for Multi-Infeed HVDC Power Systems

Multi-infeed LCC-HVDC decreases voltage stability margins due to its current source nature. Moreover, the high penetration of variable renewable energy makes it difficult to reserve enough margins for all periods. Therefore, voltage stability constraints need to be considered in the scheduling stage. However, the challenge is that the voltage stability margin is not analytic with respect to state variables. We propose a sparse support vector machine to extract the nonlinear voltage stability rule. To embed the rule into economic dispatch (ED), we apply a convex reformulating technique to the rule and then formulate a voltage stability constrained ED (VSCED) model by semi-definite programming. The model is tested on a modified IEEE-14 system and a real-world system from Jiangsu Province, China; and it is compared with other typical data-driven-based models in the Jiangsu system. The numerical experiments suggest that the stability margins can be effectively reserved above the required threshold within an acceptable computation time by the proposed VSCED model, which outperforms the other models.

Assessment of a combined heating and power system based on compressed air energy storage and reversible solid oxide cell: Energy, exergy, and exergoeconomic evaluation

The electricity grid with high-penetration renewable energy sources has urged us to seek means to solve the mismatching between electricity supply and demand. Energy storage technology could accomplish the energy conversion process between different periods to achieve the efficient and stable utilization of renewable energy sources. In this paper, a hybrid energy storage system based on compressed air energy storage and reversible solid oxidation fuel cell (rSOC) is proposed. During the charging process, the rSOC operates in electrolysis cell (EC) mode to achieve the energy storage by converting the compression heat to chemical fuels. During the discharging process, the cell operates in fuel cell mode for electricity production and the gas turbine is conducted to recover the waste heat from cell. To evaluate the comprehensive performance of the proposed system, the energy, exergy, and exergoeconomic studies are conducted in this paper. Under the design condition, the results indicated that the proposed system is capable of generating 300.36 kW of electricity and 106.28 kW of heating; in the meantime, the energy efficiency, exergy efficiency, and total cost per unit exergy of product are 73.80%, 55.70%, and 216.78 $/MWh, respectively. The parametric analysis indicates that the increase in pressure ratio of air compressor, steam utilization factor of rSOC stack under EC mode and current density of the rSOC stack under EC mode reduce exergy efficiency and total cost per unit exergy of product simultaneously, while the increment of operating pressure of rSOC stack under FC mode enhances the exergy efficiency and decreases total cost per unit exergy of product. The multi-objective optimization is carried out to improve the comprehensive performance of the proposed system, and the results expressed that the best optimal solution has the exergy efficiency and total cost per unit exergy of product of 65.85% and 187.05 $/MWh, respectively. Compared to the basic operating condition, the improvement of the proposed system has led to the maximum enhancement of 20.32% in exergy efficiency and 18.60% in total cost per unit exergy of product.

An efficient cross-entropy method addressing high-dimensional dependencies for composite systems reliability evaluation

Cross-entropy (CE) based importance sampling (IS) accelerates the reliability evaluation of power system greatly, but is mainly focused on the CEIS of independent random variables (RVs) or low-dimensional correlated RVs (CRVs). To extend the CE method to high-dimensional CRVs while avoiding the “curse of dimensionality” in optimizing a high-dimensional IS probability density function (IS-PDF), an efficient CE method termed as CE-DRDM, is developed from a dimension-reduced dependence model (DRDM). First, based on the DRDM’s original hierarchical disaggregate structure (HDS), the CE-DRDM which conducts the CEIS for a single aggregate RV rather than the high-dimensional CRVs is proposed. Second, to solve the issue that the performance of CE-DRDM may degrade in the case of high penetration of renewable energy, the CE-DRDM is enhanced by improving the DRDM’s HDS, and then a novel CE optimization method is proposed, through which the analytical updating formulas of IS-PDF parameters can be derived. Thirdly, the CE-DRDM is further enhanced by using a sparse density estimator, based on which the number of IS-PDF parameters needed to be optimized in the CE optimization reduces greatly, and consequently the optimization accuracy improves accordingly. Finally, the validity of CE-DRDM is verified by several numerical cases with high-dimensional dependencies.

Locational Role Analysis of Energy Storage Systems Based on Optimal Capacity Needs and Operations under High Penetration of Renewable Energy

As the need for energy storage systems (ESSs) capacity is increasing due to high accommodation of renewable resources, it is crucial to analyze in which location and for what purpose the ESSs are required to achieve the highest efficiency. Investors and system operators can place and operate the ESSs as expected based on this analysis. Therefore, this study assesses the specific roles of ESSs in a grid system based on their optimal capacity needs, locations, and operations. A long-term simulation model using mixed-integer programming is proposed to obtain these optimal solutions, such as ESS capacity and operational schedules for energy and reserves. Four-week operational simulations are performed for each month using data from the California Independent System Operator. ESSs are placed at sites with solar photovoltaic (PV) systems or wind farms, at baseload generator buses, and at load buses to verify the role of ESSs, depending on the locational differences. The detailed roles are analyzed from the aspects of flexible capacity supply, reserve deployments, time-shifting renewable and thermal energy generation, and costs. The results show that the ESSs on the baseload generation side provide flexibility by time-shifting baseload generation and turn on baseload generators, even when the net load is small. For instance, the required capacity of the flexible thermal generators, such as natural gas turbine generators, is about 3004 MW without the ESS operations in May. When 450 MW ESSs colocated with solar PVs are operated, the required flexible capacity of the thermal generators is lowered to 2404 MW. Moreover, ESSs are highly utilized as a downward reserve provider, although their costs for reserves are higher than thermal generators.

Empirical Model for electrical power demand in Mexico

In recent decades, the average global temperature has risen rapidly, due to the modern society activities. Hence, the United Nations has suggested that an increase of 1.5°C above the reference temperature corresponding to the beginning of industrial revolution era must be avoided. One proposal to mitigate this global warming is the migration towards green energy generation systems, however, suitable predictions of electricity demand are required to properly manage the implementation and operation of hybrid energy generation systems with high penetration of renewable energies. In this paper, a simple mathematical model based on sinusoidal functions which consider the normal time periods of the society like daily, weekly, monthly, and annual is proposed for the electricity demand in Mexico. The model parameters were obtained from the historical data from 2017 to 2021, furthermore, the model was used to estimate the electricity demand for the year 2022 as a demonstration of its usefulness, obtaining a mean absolute error of 0.9% in the whole year period. The proposed model is simple and can be used to reproduce and estimate the electricity demand of the national interconnected system.

Vehicle-to-Grid Power in Danish Electric Power Systems

The integration of renewable energy systems is often constrained by the variable nature of their output. This demands for the services of storing the electricity generated from most of the renewable energy sources. Vehicle-to-grid (V2G) power could use the inherent energy storage of electric vehicles and its quick response time to balance and stabilize a power system with fluctuating power. This paper outlines the use of battery electric vehicles in supporting large-scale integration of renewable energy in the Danish electric power systems. The reserve power requirements for a high renewable energy penetration could be met by an amount of V2G based electric vehicles less than 10% of the total vehicle need in Denmark. The participation of electric vehicle in ancillary services would earn significant revenues to the vehicle owner. The power balancing services of electric vehicles in an electricity network with a large variable renewable generation in Denmark seems feasible and realistic.

Service scheduling strategy for microservice and heterogeneous multi-cores-based edge computing apparatus in smart girds with high renewable energy penetration

The microservice-based smart grid service (SGS) organization and the heterogeneous multi-cores-based computing resource supply are the development direction of edge computing in smart grid with high penetration of renewable energy sources and high market-oriented. However, their application also challenges the service schedule for edge computing apparatus (ECA), the physical carrier of edge computing. In the traditional scheduling strategy of SGS, an SGS usually corresponds to an independent application or component, and the heterogeneous multi-core computing environment is also not considered, making it difficult to cope with the above challenges. In this paper, we propose an SGS scheduling strategy for the ECA. Specifically, we first present an SGS scheduling framework of ECA and give the essential element of meeting SGS scheduling. Then, considering the deadline and importance attributes of the SGS, a microservice scheduling prioritizing module is proposed. On this basis, the inset-based method is used to allocate the microservice task to the heterogeneous multi-cores to utilize computing resources and reduce the service response time efficiently. Furthermore, we design the scheduling unit dividing module to balance the delay requirement between the service with early arrival time and the service with high importance in high concurrency scenarios. An emergency mechanism (EM) is also presented for the timely completion of urgent SGSs. Finally, the effectiveness of the proposed service scheduling strategy is verified in a typical SGS scenario in the smart distribution transformer area.

Optimal configuration of energy storage for alleviating transmission congestion in renewable energy enrichment region

High-penetration renewable energy development causes transmission congestion in power system operation. Such transmission congestion in short period can be alleviated by energy storage configuration, instead of investing and expanding new transmission lines. This paper presents an optimal configuration method of energy storage for alleviating transmission congestion in renewable energy enrichment region. In order to obtain the characteristics of renewable energy outputs, a scenario generation technique for renewable energy outputs considering spatio-temporal correlation is first proposed. A Monte Carlo-based approach for evaluating the transmission congestion is proposed for identifying the potential locations of energy storage installation. Finally, an optimal configuration method of energy storage based on stochastic programming is proposed for alleviating transmission congestion. Numerical experiments are carried out on a modified IEEE-RTS 24-bus system and a practical 129-bus system. Numerical results show that energy storage can improve the flexibility of power system operation and the utilization of renewable energy generation. Especially, in the practical 129-bus system, the proposed method reduces renewable energy curtailment by 625.26 MWh and 444.43 MWh under fall and winter typical scenarios, respectively. And, investing some energy storage devices is more economical when the duration of transmission congestion is relatively short.

Efficient virtual power plant management strategy and Leontief-game pricing mechanism towards real-time economic dispatch support: A case study of large-scale 5G base stations

Amidst high penetration of renewable energy, virtual power plant (VPP) technology emerges as a viable solution to bolster power system controllability. This paper integrates a novel flexible load, 5G base stations (gNBs) with their backup energy storage systems (BESSs), into a VPP for power system real-time economic dispatch (RTED). Leveraging BESSs dispatchable capacity, the VPP offers power support and gains economic incentives, where the dispatchable capacity is estimated based on distribution network reliability and gNB availability requirements. Then, an efficient and refined VPP management strategy is proposed, where numerous gNBs are aggregated into limited number of virtual generators (VGs). The VPP dispatchable capacity can be efficiently and accurately evaluated via VGs states. Each VG is assigned a control cost function, enabling optimal control decisions resembling power system RTED, to minimize overall VPP control costs. Finally, a power support and incentive determination mechanism is presented based on the Leontief union bargaining model, ensuring reciprocal transactions between VPP and power system. To solve the Leontief model and address information privacy issues between the two parties, a distributed iterative algorithm is developed. Simulations based on actual data and IEEE 118-bus system illustrate that the proposed approach can smoothen intra-day load profiles, achieving mutual economic benefits for the power system and VPP without compromising the security of 5G network operations.

Scenario Reduction Network Based on Wasserstein Distance With Regularization

Power systems with high penetration of renewable energy contain various uncertainties. Scenario-based optimization problems need a large number of discrete scenarios to obtain a reliable approximation for the probabilistic model. It is important to choose typical scenarios and ease the computational burden. This paper presents a scenario reduction network model based on Wasserstein distance. Entropy regularization is used to transform the scenario reduction problem into an unconstrained problem. Through an explicit neural network structure design, the output of the scenario reduction network corresponds to the Sinkhorn distance function. The scenario reduction network can generate the typical scenario set through unsupervised learning. An efficient algorithm is proposed for continuous/discrete scenario reduction. The superiority of the scenario reduction network model is verified through case studies. The numerical results highlight the high accuracy and computational efficiency of the proposed model over state-of-the-art models, and it makes an ideal candidate for large-scale scenario reduction problems.

Sample-Adaptive Robust Economic Dispatch With Statistically Feasible Guarantees

The high penetration of renewable energy brings significant uncertainty to the power grids. Taking economic dispatch (ED) as an example, the inaccurate prediction of renewable energy generations dramatically increases the dispatch cost and risks the power grid's reliable operation. The accurate distribution knowledge of the renewable generations enables modeling the ED as stochastic programming with joint chance constraints, which various classical methods can tackle. However, in practice, such distribution knowledge is inaccessible, and we can only observe samples from some unknown distribution. This makes conducting effective ED solely based on the observed samples challenging. It is particularly true when we need to handle the joint chance constraints. To tackle these challenges, we introduce the notions of statistical feasibility and statistically feasible ED to guarantee the satisfaction of the joint chance constraints. Specifically, we first propose a sample-adaptive robust optimization (RO) to decouple the joint constraints. We then identify that the inaccurate uncertainty set leads to RO's conservativeness, and then reconstruct the constraint-specific uncertainty sets. We design the corresponding sample-adaptive reconstruction-based RO (ReconRO) based on the reconstructed uncertainty sets to further enhance the ED's effectiveness.