Renewable Energy Penetration Research Articles

Estimating the impacts of a new power system on electricity prices under dual carbon targets

The construction of a new power system dominated by renewables is crucial for achieving China's goals of carbon emission peaking and carbon neutrality. While numerous studies have investigated grid parity for large-scale renewables, some have overlooked the fact that the integration of variable renewable energy requires coordination with regulating resources. This coordination introduces additional costs, leading to subsequent increases in electricity prices. Our study comprehensively considers the total system costs and proposes a dynamic recursive linear optimization model. This model aims to simulate both short-term and long-term impacts of constructing a new power system on electricity prices in China. It evaluates the short-term effects of the Russia-Ukraine conflict and the COVID-19 pandemic on energy prices and demand, as well as the long-term effects of renewable energy penetration on ancillary service costs and technological progress on system costs. The results show that China's electricity price is projected to rise by 10.2% in 2030 for carbon peaking. By 2060, with an 83.3% renewable energy penetration for carbon neutrality, the electricity price will reach 0.859 CNY/kWh, representing a 27.2% increase. Ancillary service costs will experience the highest growth rate, accounting for 15.5% of system costs by 2060. Our study also proposes optimal technological pathways for the new power system and provides valuable data to support the development of effective electricity price mechanisms.

An Accurate Ladder-Type Ramp Rate Constraint Derived From Field Test Data for Thermal Power Unit With Deep Peak Regulation

The power system experiences a significant increase in its flexibility in recent years, along with the increase in renewable energy penetration. Implementing the flexibility retrofit of thermal power units (TPUs) is an effective way to enhance power system flexibility. However, the conventional ramp rate constraint cannot reveal the ramping characteristics of TPUs during deep peak regulation after the retrofit. In this paper, to establish a constraint that is more consistent with the engineering practice, the relationship between ramping capability and unit load of TPUs is firstly analyzed, based on the field test data of a TPU in China. Then, a ladder-type ramp rate of the TPU is proposed before a unified expression for the constraint is derived for the first time. When a specific dispatch time scale is satisfied, an equivalent expression for this constraint is also derived. Finally, the dimensionality of the binary variables in ladder-type ramp rate constraint is discussed, and the effectiveness of the ladder-type ramp rate constraint of the TPU is initially verified, with the actual 1-year operating data of two TPUs of a power plant in Dalian, Liaoning Province, China.

Investigating the Effect of Green Finance Initiatives on Renewable Energy Penetration in Europe

Investigating the Effect of Green Finance Initiatives on Renewable Energy Penetration in Europe

A distributionally robust energy management of microgrid problem with ambiguous chance constraints and its tractable approximation method

As the penetration of intermittent renewable energy increases in microgrid systems, flexible power generation resources cause the power imbalance problem. To improve the absorption of renewable energy and to deal with its uncertainty more effectively, this paper proposes a distributionally robust approximate model for microgrid with ambiguous chance constraints (DR-CCP). First, the distributionally robust model with ambiguous chance constraints is a semi-infinite chance constrained planning problem, which is computationally difficult and inefficient, so the use of Chernoff's inequality to derive a safe tractable approximation form for the ambiguous chance constraint on the basis of a probability distribution for ambiguous sets including bounded perturbations with mean zero, transforms it into a mixed integer linear programming problem that can be directly solved using CPLEX to solve it. Then, the optimal value of the proposed model is used to approximate equivalent conditional value at risk (CVaR), and the relationship with CVaR is established. The optimal solution of our model is employed to construct multiple sets of comparison models, enhancing the richness of numerical experiments. Finally, to validate the feasibility and effectiveness of our proposed model, a series of diverse tests are performed on the IEEE 33-bus power distribution system.

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.

Realizing ampere-level CO2 electrolysis at low voltage over a woven network of few-atom-layer ultralong silverene nanobelts with ultrahigh aspect ratio by pairing with formaldehyde oxidation.

The development of advanced multi-functional electrocatalysts and their industrial operation on paired electrocatalysis systems presents a promising avenue for the gradual penetration of renewable energy into practical production. Herein, a self-supported conductive network of silverene nanobelts (Ag-ene NBs) was delicately assembled (Ag-NB-NWs), in which ultralong and few-atom-layer Ag-ene NBs with a high edge-to-facet ratio were interconnected, serving as "superreactors" for electron transfer and mass transport during the reaction. Such superstructures as electrocatalysts delivered an unparalleled performance toward the CO2-to-CO conversion with exclusively high faradaic efficiency (FE) and partial current densities of up to 1 A cm-2. Remarkably, the membrane electrode assembly (MEA) cell with Ag-NB-NWs as the cathode was capable of ultrastable and continuous operation for over 240 h at 0.4 A with ∼100% selectivity. More importantly, by further using Ag-NB-NWs as a bifunctional electrocatalyst, a record-low voltage overall CO2 electrolysis system coupling cathodic CO2 reduction with anodic formaldehyde oxidation in MEA cell was performed to achieve concurrent feed gas generation and formate production, substantially improving electrochemical techno-economic feasibility.

Integrating climate policies in the sustainability analysis of green chemicals.

New and enhanced processes will not be the only drivers toward a sustainable chemical industry. Implementing climate policies will impact all components of the chemical supply chain over the following decades, making improvements in energy generation, material extraction, or transportation contribute to reducing the overall impacts of chemical technologies. Including this synergistic effect when comparing technologies offers a clearer vision of their future potential and may allow researchers to support their sustainability propositions more strongly. Ammonia and methanol production account for more than fifty percent of the CO2 emissions in this industry and are, therefore, excellent case studies. This work performs a prospective life cycle assessment until 2050 for fossil, blue, wind, and solar-based technologies under climate policies aiming to limit the global temperature rise to 1.5 °C, 2 °C, or 3.5 °C. The first finding is the inability of fossil-based routes to reduce their CO2 emissions beyond 10% by 2050 without tailored decarbonisation strategies, regardless of the chemical and climate policy considered. In contrast, green routes may produce chemicals with around 90% fewer emissions than today and even with net negative emissions (on a cradle-to-gate basis), as in the case of methanol (up to -1.4 kg CO2-eq per kg), mainly due to the contributions of technology development and increasing penetration of renewable energies. Overall, the combined production of these chemicals could be net-zero by 2050 despite their predicted two to fivefold increase in demand. Lastly, we propose a roadmap for progressive implementation by 2050 of green routes in 26 regions worldwide, applying the criterion of at least 80% reduction in climate change impacts when compared to their fossil alternatives. Furthermore, an exploratory prospective techno-economic assessment showed that by 2050, green routes could become more economically attractive. This work offers quantitative arguments to reinforce research, development, and policymaking efforts on green chemical routes reliant on renewable energies.

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.

Dynamics Islanding Control for Power Grid with High Penetration of Renewable Energy

Dynamics Islanding Control for Power Grid with High Penetration of Renewable Energy

Design flexible renewable energy penetrated power system to address long-run and short-run interactive inference

<p>For power systems with a high penetration of renewable energy, sufficient flexible resources such as energy storage must be combined to achieve sustainable energy development. However, in the planning of flexible resources, external societal factors can significantly change the evolution pathways of these resources. A simulation framework is urgently needed to integrate long-run development factors represented by societal influences, with the short-run operational characteristics within the physical energy framework. We provide technical support for the sustainable development of power systems, making the simulation results more accurate for future energy system planning. To address the lack of data for modeling external societal factors, a long-run modeling method based on system dynamics is proposed, alongside a short-run modeling method considering flexibility assessment and optimization. Long-run external societal factors necessitate a low-carbon system, while short-run concerns involve the actual topology of the power system to investigate high flexibility. We found that the sensitivity of various flexibility resource investments to both flexibility and low carbon in the power system is key to resolve this contradiction. An empirical calculation of the power system is conducted in the 213-bus flexibility test system including real data at 15-minute and 1-minute resolutions. Battery storage becomes the largest flexibility investment about a decade after the introduction of carbon reduction policies. While other flexibility resources particularly demand-side response due to unsaturated flexibility, also become major temporary investment assets. Considering the proposed interactive inference framework, there was a significant reduction in marginal abatement cost, and carbon trading continuously reduces the abatement cost.</p>

Promotion of distributed energy system considering capacity allocation optimization and multi-stakeholder decision-making behaviors

The promotion of distributed energy systems with hybrid renewable energy needs careful considerations on multiple conflicting objectives (e.g. economy, environment and energy efficiency) and multiple stakeholders (e.g. government, energy enterprises and energy users) from planning and market operation stage. Firstly, the capacity allocation for a park-level distributed energy system is analyzed by using the multi-objective optimization model, and the optimal capacity allocations under different carbon tax constraints are determined. Then, an evolutionary game model is developed to determine evolutionarily stable strategies for promoting the distributed energy system. The results show that the carbon tax contributes to increasing the penetration of renewable energy. When increasing the carbon tax from zero to 11.6% of the average electricity price, the annual carbon emission can decrease by 2.9%. At the initial stage, enterprises would prefer distributed energy system as the subsidy increases to 42% of the additional cost due to producing distributed energy system. At the accelerated stage, when the carbon tax due to producing conventional system exceeds 2.7% of the additional cost, distributed energy system becomes more economical and environmentally friendly. At the stable stage, when the policy implementation cost is higher than the corresponding social benefits, the dominance of government declines.

Multi-Dimensional Economy-Durability Optimization Method for Integrated Energy and Transportation System of Net-Zero Energy Buildings

With the increasing penetration of renewable energy and zero-emission vehicles (ZEVs), the integrated energy and transportation system (IETS) is considered a promising solution for achieving net-zero energy buildings (NZEBs). However, the tight coupling and the conflict of interests between the transportation and energy systems increase the difficulty of collaborative operation scheduling. Therefore, this paper proposes a multi-dimensional economy-durability (MDED) optimization method for IETS of NZEBs. The multidimensionality includes multiple subjects of energy, transportation, and IETS, and multiple time scales of day-ahead and intraday. In the day-ahead stage, the comprehensive cost-optimal scheduling strategy with joint intelligent parking lot (IPL) is constructed for the collaborative operation of IETS. In the intraday stage, two scheduling strategies are applied to satisfy the operation of the transportation and energy systems, respectively. For the transportation system, a coordinated scheduling strategy based on charging service satisfaction (CSS) is presented to coordinate ZEVs demands with diverse vehicle types and service manners. And for the energy system, a rolling optimization strategy based on a dynamic economy-health matrix (DEHM) is designed to track the day-ahead scheduling plan. The results indicate that the proposed method resolves the conflict between the charging needs and the comprehensive cost of the IETS system. In addition, the economic potential and coordination capability of fast charging services in IETS coordinated scheduling are verified. Finally, the proposed DEHM has significant advantages for the system cost and health compared to the conventional weighting matrix.

Parallel Dual-DQAM for Multi-Scenario Stochastic Economic Dispatch Model by Temporal and Scenario Decompositions

Large-scale multi-scenario stochastic economic dispatch (SED) is hard to directly solve due to the huge number of variables and constraints. To reduce the computational burden, a nested dual-DQAM (Diagonal Quadratic Approximation Method) is proposed in this paper to decouple the SED problem in both scenarios and time periods, where each subproblem only contains one time period and one scenario. Moreover, these subproblems can be handled in parallel, such that the computational performance can be significantly improved. Besides, we have investigated the optimal policy to select the best parallel structure of the proposed dual-DQAM, and the theorical convergence performance is proved. Numerical results on several test systems show the effectiveness of the proposed dual-DQAM. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Nowadays, the safe operation and economic dispatch in power system are greatly challenged by the high penetration of renewable energy. Although the uncertainty of renewable energy can be well modeled by stochastic programming, the solution complexity will seriously increase due to the large number of typical scenarios. For this problem, the parallel solution using decomposition methods is the state-of-the-art strategy. In this paper, we propose an efficient solution to the multi-scenario SED problem by temporal and scenario decompositions. It realizes an important innovation in both reducing the computational burden and improving the solving efficiency, which greatly promotes the application of SED in large-scale systems. To better use this method, the following two properties should be highlighted: i) the proposed method has a convergence guarantee and works especially well on SED due to the sparsity property of linking constraint matrices. ii) the computational efficiency of the proposed method becomes more significant as the number of time periods and scenarios grows and can be further improved under a fast ramp rate.

Design and Experimental Implementation of a Robust H∞ Control for LCL Grid-connected Converter

Higher penetration of renewable energy into the electrical grids using grid-connected converters requires robust control schemes to ensure the stable behavior of these converters during disturbances on the grid side. This paper proposes a robust H∞ control scheme reinforced by the D-stability technique using linear matrix inequality (LMI) approach for a three-phase two-level grid-connected converter with LCL filter. The design performance is validated both by simulations and experimental tests. The results indicate that the H∞ control provides better response during disturbances in the grid such as variations in the grid voltage and short-circuit faults when compared to that of the conventional vector control scheme. An experimental validation is carried out by implementing the control scheme on an OPAL-RT-based realtime system to control the power injection of a grid-connected converter hardware setup. The experimental results match the simulation results and show the efficient performance of the proposed control scheme in providing the desired active/reactive power.

Data-Driven Real-Time Congestion Forecasting and Relief With High Renewable Energy Penetration

Data-Driven Real-Time Congestion Forecasting and Relief With High Renewable Energy Penetration

Inertial Estimation in Power Grids Considering Load Contribution Based on FF-UKF

Abstract In modern power systems, the extensive integration of renewable energy sources leads to a reduction in system inertia, thereby affecting grid stability. This study proposes a novel method for estimating grid inertia, which, under quasi-steady state conditions, effectively incorporates load contribution by combining class noise signals collected by PMUs (Phasor Measurement Units) with the Forgetting Factor Unscented Kalman Filter algorithm (FF-UKF). The research initially analyzes the characteristics of various inertia sources in power systems dominated by renewable energy. Building on this, we propose a grid node equivalent inertia estimation model based on node equivalent frequency response, developing an FF-UKF-based algorithm that allows for the continuous dynamic estimation of inertia at various grid nodes. Simulation experiments conducted within the IEEE 39 bus system validate the method’s effectiveness and demonstrate the spatiotemporal distribution of grid inertia under varying levels of renewable energy penetration. This study is significant for understanding and enhancing frequency control and stability in power systems, providing key technical support for the modernization of the power system and the efficient integration of renewable energy.

Impacts of High Renewable Energy Penetration in Low Voltage Distribution Network: A Review

The use of fossil fuels for electricity production is decreasing globally due to the green revolution in the energy industry. By introducing renewable energies (RE) into low voltage distribution networks (LVDN), the conventional power system structure is altered through bidirectional power flow, which turns consumers into prosumers and decentralizes the electricity market by allowing domestic end users to use their own energy as needed and sell excess to energy grids. The prosumer grid, with added grid battery storage technologies in the future, will contribute to virtual power plants (VPP) capable of supporting neighbouring communities as a reliable source during normal demands and in times of emergency backups. Thus, the concentration of research is increasing in the domain of integration of RE in the LVDN structure for a standard flexible operation. In this paper, a brief review is carried out on the effects of integrating renewable energy into low-voltage distribution networks (LVDNs), examining its impacts on stability, protection systems, voltage, and power quality. Additionally, it discusses the conventional control strategies for managing these impacts and discusses their respective advantages and disadvantages.

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.

PLANNING STUDY OF HYBRID POWER PLANT SOLAR PV-DIESEL GENERATOR ON KODINGARE ISLAND, SINJAI REGENCY

Kodingare Island is located in Pulau Sembilan District, Sinjai Regency, one of nine islands. Currently, most people still rely on conventional energy from diesel power plants. The reason is that this island does not yet receive an electricity supply from the electricity grid due to the geographical limitations of the archipelago. It is known that the most potential renewable energy source on Kodingare Island is solar energy, with a potential for solar radiation reaching 5.86 kWh/m2/day. This research aims to analyze an innovation that combines PV and solar, where PV acts as the main electricity generator, while solar functions as a backup and additional energy source. The method used in this research uses simulation methods, layout modeling, and financial analysis using HOMER Pro simulation software to determine the potential and performance of hybrid power plants and SketchUp Pro software to produce three-dimensional layouts and economic and feasibility values obtained through financial analysis. Technical aspects include producing an electrical energy system of 37,029 Wh/year, consisting of PV of 32,981 Wh/year and solar of 4,048 Wh/year with energy consumption of 33,850 Wh/year. The required fuel consumption is 2,086 L/year, with excess electricity of 931 kWh/year and renewable energy penetration of 89.1%. From an economic perspective, planning this hybrid power system requires an investment of 258.290.000 IDR, O&amp;M costs of 19.350.600 IDR, and the cost of energy value of 1,352/kWh IDR. In contrast, from the feasibility aspect of planning a hybrid electric power system, it is said to be feasible because it produces a Net Present Value of 9,870,151 IDR, is more significant than zero, the Profitability Index is 1.03 greater than one, the Internal Rate of Return is 8.90% greater than the credit interest rate of 8.43% and the Payback Period required for return of capital is nine years nine months.

Stochastic security‐constrained transmission and energy storage expansion planning considering high penetration of renewable energy in integrated gas‐electricity networks

AbstractThis paper presents a new formulation for solving the expansion planning of transmission lines and energy storage systems while considering the integration of electricity and gas networks. The proposed model is a bi‐level stochastic planning model. It involves transmission and battery expansion planning at one level, and gas network modeling at the other. The study addresses the impact of high penetration of renewable resources and security constraints on both the electricity and gas networks within the context of network expansion planning. The proposed model is a stochastic mixed‐integer linear programming model at both levels, and its challenging solution is achieved through reformulation and decomposition methods. Two experimental networks are analyzed: a 6‐node network and the IEEE RTS 24‐bus network for the electricity network, coupled with 5‐node and 10‐node gas network systems. The results demonstrate the efficiency of the proposed model. Simulation results indicate that the proposed model is highly effective in scenarios where power and gas network lines are disconnected, preventing load shedding even when the integrated network lines are disconnected.