Penetration Of Renewable Generation Research Articles

Performance Analysis of a Backward/Forward Algorithm Adjusted to a Distribution Network with Nonlinear Loads and a Photovoltaic System

Context: The backward/forward (BF) algorithm is a sweep-type technique that has recently been used as a strategy for the power flow analysis of ill-conditioned networks. The purpose of this study is to evaluate the performance of the BF algorithm compared to that of a computational tool such as Simulink, with both strategies adjusted to the operating conditions of a distribution network with nonlinear components (loads and photovoltaic system), unbalanced loads, and harmonic distortion in the voltage and current signals. Method: The study case is a low-voltage distribution network with a radial topology, unbalanced loads, and nonlinear components. The BF algorithm is adjusted to consider two approaches of the Norton model: a coupled admittance matrix and a decoupled admittance matrix. The latter is also used in the network model created in Simulink. The performance of the algorithm is evaluated by analyzing 18 operation scenarios defined according to the presence and use intensity of the loads and solar irradiance levels (low and high). Results: In general, the three strategies could successfully determine the waveform and RMS values of the voltage signals with errors of less than 0,8 and 1,3%, respectively. However, the performance of the strategies for the estimation of current signals and power parameters shows errors of 5-300% depending on the level of solar irradiance at which the photovoltaic system operates. Conclusions: The results show that the BF strategy can be used to analyze unbalanced power grids with increasing penetration of renewable generation and the integration of nonlinear devices, but the performance of this strategy depends on the load model applied to represent the behavior of nonlinear devices and generation systems.

Unlocking market secrets: Revealing wholesale electricity market price dynamics with a novel application of spectrum analysis

Understanding market participants' competitive behaviour is essential for optimising financial performance in liberalised electricity markets. However, this is challenging due to complex market structures, generation dependent on different primary energy sources and lack of transparency. This paper introduces a novel approach using power spectrum analysis applied to wholesale electricity markets to uncover hidden patterns. Applying this novel method to the Western Australian Wholesale Electricity Market (WEM) revealed periodic cycles in different fuel types and technologies that offered insights into competitor behaviour not immediately evident in the dataset. Surprisingly, the approach uncovered that in a power system with high penetration of renewable generation, there is a weak price response to demand changes, challenging assumptions about the direct link between demand and price formation. These insights could be applied gain a competitive edge in capital investment decisions and tactical bidding behaviour.

Optimal operation of smart energy hub considering high-temperature heat and power storage

Due to the impacts of high intermittent renewable resources, increasing their penetration in the generation sector of energy systems is still challenging. In this regard, the integration of different energy networks can play a key role in dealing with this challenge. This paper focuses on the analysis and optimization of multi-generation energy storage (MGES) system’s performance and investigates the role of this type of energy storage in the operation of future smart energy systems by considering 100% green energy goals. Hence, this paper first introduces high-temperature heat and power storage (HTHPS) system, as a novel MGES unit, for a local integrated energy system (IES) with different energy carriers. Then, a novel optimal energy scheduling scheme for this system is presented by considering the energy hub concept. The proposed IES is introduced as a Smart Energy Hub (SEH) which supplies local energy demands through local renewable resources generation, as well as upstream energy networks, by considering a competitive energy market. Finally, the performance of the proposed SEH is investigated by simulating different case studies and its effectiveness is proved in increasing the renewable generation penetration.

Modeling the Reserve Capacity of Wind Power and the Inherent Decision-Dependent Uncertainty in the Power System Economic Dispatch

The ever-increasing penetration of renewable generation is demanding additional flexibility for the operation of power systems. In this sense, it has been proved that wind power generators (WPGs) can provide part of the required operating reserve through pitch angle control (PAC) under a deloaded strategy. Unlike conventional operating reserve sources, however, the operating reserve provided by WPGs can't always be considered reliable, which renders the power system dispatch more challenging. In this research, the reserve capacity of the WPGs is modeled considering the decision-dependent uncertainty (DDU). The random wind speed introduces uncertainty to the available reserve capacity of the WPGs while the decisions on the deloading degrees of the WPGs directly affect their coefficients between wind speed and usable wind power, making the uncertainty decision-dependent. Moreover, the operational technical limits of the WPGs and the multiple possible wind speed scenarios are also considered in the wind power reserve capacity modeling. Then, a two-stage reliability constrained stochastic economic dispatch model with WPGs' reserve provision (SED-WRP) is developed to jointly dispatch the generation and reserve. A group-based multi-cut Benders decomposition method is applied to improve the convergence performance of the solution algorithm. Case studies are solved to quantify the impact of considering the reserve provision of WPGs and the inherent DDU in power system dispatch decisions.

Expansion planning of the transmission network with high penetration of renewable generation: A multi-year two-stage adaptive robust optimization approach

This paper addresses the multi-year two-stage expansion planning of the transmission network of a power system with high penetration of renewable generation modeling long-term uncertainty by using adaptive robust optimization. The multi-year nature of the problem is modeled by considering a comprehensive view of the planning horizon. A cardinality-constrained uncertainty set is used to model the future worst-case uncertainty realization of the peak power consumption of loads, along with the capacity and marginal production cost of generating units. Unlike previous works, we model certain features of the operation that are typically ignored in multi-year robust transmission network expansion planning problems, namely, the operational variability of renewable generating units, the operational flexibility of conventional generating units, and the non-convex operational feasibility sets of storage facilities. The solution procedure employed for this multi-year two-stage robust problem, which is formulated as a three-level problem, is based on the combination of the nested column-and-constraint generation algorithm with two exact acceleration techniques. We analyze the performance of the proposed model through the use of the IEEE 24-bus Reliability Test System and the IEEE 118-bus Test System. Numerical results show that the use of the multi-year approach leads to reductions in the total worst-case cost of up to 7% in comparison with the static and sequential static procedures. Moreover, an underestimation of the total worst-case cost of more than 8% is attained when ignoring certain operational constraints of conventional generating units and storage facilities. Lastly, a sensitivity analysis is presented in order to illustrate the impact of the maximum deviations of the uncertain parameters on the total worst-case cost.

Probabilistic Forecasting of Available Load Supply Capacity for Renewable-Energy-Based Power Systems

In order to accurately analyze the load supply capability of power systems with high penetration of renewable energy generation, this paper proposes a probabilistic available load supply capability (ALSC) forecasting method. Firstly, the optimal input features are selected by calculating the maximal information coefficient (MIC) between the input features and the target output. Based on this, a stacking ensemble learning model is applied for the prediction of wind power, photovoltaic power and load power. Secondly, the distributions of the forecasting objects are obtained based on forecasting errors and the error statistics method. Finally, the forecasting distributions of wind power, photovoltaic power and load are set as the parameters of a power system, and then probabilistic ALSC is calculated using Latin hypercube sampling (LHS) and repeated power flow (RPF). In order to simulate a more realistic power system, multiple slack buses are introduced to conduct two types of power imbalance allocations with novel allocation principles during the RPF calculation, which makes the ALSC evaluation results more reasonable and accurate. The results of probabilistic ALSC forecasting can provide a reference for the load power supply capacity of a power system in the future, and they can also provide an early warning for the risk of ALSC threshold overlimit. Case studies carried out on the modified IEEE 39-bus system verify the feasibility and effectiveness of the proposed methods.

Primary frequency response from hydrogen-based bidirectional vector coupling storage: modelling and demonstration using power-hardware-in-the-loop simulation

To meet reduction targets for carbon emissions and improve the flexibility and security of the energy supply, future energy networks will require enhanced energy vector coupling in addition to the generation of energy from renewable sources. Increased renewable generation penetration significantly affects the electrical grid’s inertia and consequently the severity and regularity of frequency deviations from nominal values. Bidirectional Hydrogen-based Vector Coupling Storage (VCS) has been explored as a means to provide primary frequency response (PFR) services to the electrical network. This paper demonstrates the use of Power Hardware-In-the-Loop (PHIL) simulation and Digital Twin (DT) technique for such an application. This new suggested structure of VCS is composed of grid-scale electrolysers, fuel cells, and hydrogen storage. Existing works focus on unidirectional VCS, and also use simplifications or neglect the impacts of power converters on the performance of the VCS. In addition, these works do not have any control over the hydrogen storage, therefore there is no guarantee that there will be enough energy available in the storage to meet the PFR service responsibilities. This paper presents the dynamic models of electrolysis, fuel cell stacks, and hydrogen storage as a DT. The key parameters affecting the behaviours of these main components are considered. The power converters’ accurate impact on the VCS’s performance is considered through PHIL simulations. The level of stored hydrogen is also considered in the VCS controller. The DT representing the VCS is integrated with the PHIL setup representing the deployment environment. The impact of VCS is then analysed as it propagates to the deployment environment. Results of the considered case studies demonstrate that the size of the VCS plays a significant role in bringing the frequency to the statutory allowed range. In addition, more VCS capacity was installed, the nadir frequency improved. Furthermore, the VCS is fast enough to offer PFR. The response times of the VCS were 2.857 s (during under-frequency periods), corresponding to the operation of the fuel cells, and 2.252 s during over-frequency periods, corresponding to electrolyser operation.

Optimal Electricity Imbalance Pricing for the Emerging Penetration of Renewable and Low-Cost Generators

Problem definition: With the rise of renewables and the decline of fossil fuels, electricity markets are shifting toward a capacity mix in which low-cost generators (LCGs) are dominant. Within this transition, policymakers have been considering whether current market designs are still fundamentally fit for purpose. This research analyses a key aspect: the design of real-time imbalance pricing mechanisms. Currently, markets mostly use either single pricing or dual pricing as their imbalance pricing mechanisms. Single-pricing mechanisms apply identical prices for buying and selling, whereas dual-pricing mechanisms use different prices. The recent harmonization initiative in Europe sets single pricing as the default and dual pricing as the exception. This leaves open the question of when dual pricing is advantageous. We compare the economic efficiency of two dual-pricing mechanisms in current practice with that of a single-pricing design and identify conditions under which dual pricing can be beneficial. We also prove the existence of an optimal pricing mechanism. Methodology/results: We first analytically compare the economic efficiency of single-pricing and dual-pricing mechanisms. Furthermore, we formulate an optimal pricing mechanism that can deter the potential exercise of market power by LCGs. Our analytical results characterize the conditions under which a dual pricing is advantageous over a single pricing. We further compare the economic efficiency of these mechanisms with respect to our proposed optimal mechanism through simulations. We show that the proposed pricing mechanism would be the most efficient in comparison with others and discuss its practicability. Managerial implications: Our analytical comparison reveals market conditions under which each pricing mechanism is a better fit and whether there is a need for a redesign. In particular, our results suggest that existing pricing mechanisms are adequate at low/moderate market shares of LCGs but not for the high levels currently envisaged by policymakers in the transition to decarbonization, where the optimal pricing mechanism will become more attractive.Funding: R. Belo acknowledges funding by Fundação para a Ciência e a Tecnologia [UIDB/00124/2020, UIDP/00124/2020 and Social Sciences DataLab – PINFRA/22209/2016], POR Lisboa, and POR Norte [Social Sciences DataLab, PINFRA/22209/2016].Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2021.0555 .

Methods to improve reliability and operational flexibility by integrating hybrid community mini-grids into power systems

Decarbonization and decentralization are major trends in the transformation of conventional power systems. The increase of the penetration of renewable electricity generation into generation mix contributes to the reduction of greenhouse gas emissions. At the same time, one has to ensure that the reliability and operational flexibility as measured by respective metrics meet specified standards. This calls for a solution of a set of engineering, economic, and institutional problems, the list of which depends on the features specific to countries and regions in question. This is due to differences in the requirements established by technical regulations, the rules governing wholesale and retail electricity and capacity markets, the mix of electricity consumption, as well as the principles underpinning the management of capacity expansion and operation of power systems. The purpose of this article is to provide the rationale for the feasibility and efficacy of integrating hybrid community mini-grids into power systems. The balance of community mini-grids in terms of power and capacity allows them to operate both in the grid-connected and islanded modes without disrupting power supply to consumers. The main sources of electricity in hybrid community mini-grids are distributed generation (DG) facilities, including those based on renewables as well as energy storage systems (ESS). Load dispatching of community mini-grids is performed by an intelligent automatic control system (ACS) governed by decentralized algorithms, which provides coordinated operation of emergency control and power flow automatics. Integration of community mini-grids into power systems can be accomplished through several transmission lines that form an interconnection. The controlled flow of power through such an interconnection allows for significant system-wide technical and economic effects. We propose a technique to provide the rationale for the efficacy of creating community mini-grids. The technique is based on calculations of reliability metrics. The integration of hybrid community mini-grids into power systems makes it possible to increase the availability and uninterrupted supply of electricity to consumers, as well as the lifetime of power grid equipment.

Demand Side Management in Industrial, Commercial, and Residential Sectors: A Review of Constraints and Considerations

Demand Side Management (DSM) is a cost-effective approach to managing electricity networks, aimed at reducing capacity requirements and costs, increasing the penetration of renewable generation, and reducing power system emissions. This review article explores the distinctive characteristics of electricity demand in the industrial, commercial, and residential sectors, and their relationship to successful implementation of DSM. The constraints and considerations for DSM are characterized as technical, economic, and behavioral factors, such as process requirements, business operation constraints, and consumer decisions, respectively. By considering all three types of factors and their impacts in each sector, this review contributes novel insights that can inform the future implementation of DSM. DSM in the industrial and commercial sectors is found to be primarily constrained by technical considerations, while DSM in the commercial sector is also subject to economic constraints. Conversely, residential demand is found to be primarily constrained by human behavior and outcomes, highly variable, and the largest contributor to peak demand. This review identifies sector-specific opportunities to enhance DSM uptake. Industrial DSM uptake will benefit from technological and process improvements; commercial DSM uptake can benefit from enhanced economic incentivization; and residential DSM uptake can benefit from improved understanding of the interactions between human behavior, human outcomes, and energy use. Finally, this review investigates behavioral models and concludes that agent-based models are best suited for integrating these interactions into energy models, thereby driving the uptake of DSM, particularly in the important residential sector.

Transient Synchronization Stability of Grid-Forming Converter During Grid Fault Considering Transient Switched Operation Mode

With the increasing penetration of renewable energy generation, grid-forming (GFM) converters that simulate the dynamics of synchronous generator (SG) and actively participate in grid voltage and frequency regulation are increasingly employed. In GFM converter dominated power system, similar transient synchronization stability that is common in conventional power system also exists, which is closely related with the control dynamics of GFM converters. In this paper, the transient synchronization stability of GFM converters with virtual synchronous generator (VSG) control is focused on, with the influence of transient switched operation mode (TSOM) introduced by current saturation limitation considered especially. First, a novel transient equivalent motion model of VSG is developed. The model is a switched system and TSOM significantly changes the power angle characteristics. Based on power angle curve analysis and equal area method, the transient stability analysis is carried out from non-existence of equilibrium point and non-matching of accelerating-decelerating area. Analysis indicates that TSOM imposes another constraint to equilibrium point and reduces deceleration area, which worsens the transient stability. Further, employing the stability region theory of nonlinear dynamic systems, the stability region of VSG is visually displayed in phase plane. It is identified that the TSOM reduces the stability region and worsens the stability. Finally, the influence factors including power reference, grid voltage sag and grid impedance are discussed.

A Review of Modeling and Applications of Energy Storage Systems in Power Grids

As the penetration of variable renewable generation increases in power systems, issues, such as grid stiffness, larger frequency deviations, and grid stability, are becoming more relevant, particularly in view of 100% renewable energy networks, which is the future of smart grids. In this context, energy storage systems (ESSs) are proving to be indispensable for facilitating the integration of renewable energy sources (RESs), are being widely deployed in both microgrids and bulk power systems, and thus will be the hallmark of the clean electrical grids of the future. Hence, this article reviews several energy storage technologies that are rapidly evolving to address the RES integration challenge, particularly compressed air energy storage (CAES), flywheels, batteries, and thermal ESSs, and their modeling and applications in power grids. An overview of these ESSs is provided, focusing on new models and applications in microgrids and distribution and transmission grids for grid operation, markets, stability, and control.

State Identification of Power System Based on Visual Geometric Group Network

Accurate and timely identification of power system operation states are requisite for taking control strategies and ensuring power system security. With the high grid penetration of renewable energy generation and wide application of power electronic equipment, power system is becoming more complicated and facing more uncertainties. Traditional model based power system state identification methods are faced with challenges and data driven methods have great prospects. This paper proposes a new data-driven method for identifying power system operating states based on visual geometric group network VGG16 (16-layer Visual Geometry Group (VGG16). Firstly, the Lasso algorithm is used to select the operating state features of the power system to realize the features dimensionality reduction. Then, the weights of the loss functions are adjusted according to the difficulty of model for identifying power system states. Finally, the IEEE39 node system is used as an example, the effectiveness and robustness of the proposed method is verified.

Cloud and Edge Computing for Smart Management of Power Electronic Converter Fleets: A Key Connective Fabric to Enable the Green Transition

The increasing grid penetration of renewable energy generation, energy storage, and controllable electronic loads [e.g., electric vehicle (EV) chargers, motor drives, and electrolyzers) is making power electronic converters (PECs)—their controllable grid connection interfaces—omnipresent. Therefore, fleets of PECs are becoming key players not only in coordinating the generation and storage but also in unlocking the potential for flexibility within each load. In this article, we discuss how cloud-based platforms and edge devices can allow for interfacing PECs and advanced control algorithms. We present seven different study cases using our own developed cloud-based platform that demonstrate the smart coordination of PECs in a variety of prototypes and real-world solutions. Moreover, the capabilities of cloud and edge computing are highlighted throughout the article at large, effectively characterizing cloud-based control of fleets of PECs as an essential part of the future energy sector.

Iterative memory-driven load forecast network model for accuracy improvement

High penetration of renewable energy generation and intensive application of new-type load control measures in power system greatly increase the difficulty of load forecast (LF). Long-term dependencies in load series limit LF accuracy improvement of back propagation neural network (BPNN) and its combination variants. Inspired by coexisting features of long-term trend and non-trend fluctuation in load series, a novel iterative memory-driven load forecast network (IMDLFN) model is proposed to forecast system-level load in the scenario. It creatively adopts memory-driven units to convey long-term trend information learned from the previous time steps and to autonomously balance long-term memory and short-term memory, which effectively deals with long-term dependencies and perfectly circumvents weight selection problem of BPNN combination variants. LF for municipal power system in China demonstrates that the proposed model can remarkably improve LF accuracy especially for trend/non-trend coexisting load series.

Reactive power potential of converter-connected renewables using convex power flow optimization

Converter-connected renewable energy sources (RES) in the distribution network alter the power flow in the network. Often, grid reinforcements and additional local reactive power sources are needed to maintain steady state network operation. Converter-connected RES is an accessible local reactive power source for distribution system operators to optimize network operations. However, the non-linearity and non-convexity in the converter equations make their modelling challenging, which further results in the under-utilization of the reactive power capability from these sources. Oftentimes, the converter-connected generator is partially modelled using only apparent power constraints. This paper presents an approach based on Schur’s complement and piecewise planar approximation to incorporate the converter equations in a convex power flow optimization routine. The advantages of employing complete converter capabilities for optimal operation of the distribution network are studied via simulations on a real distribution network dataset. A comparison between deploying reactive power according to the minimum grid codes requirement, partial modelling of the converter, and complete converter capabilities is correspondingly presented. Active power losses decrease by 4.1% over 10 month period with high penetration of renewable generation. Furthermore, the voltage profile in the distribution network is improved and the reactive power dependency on the transmission network is reduced.

Probabilistic Assessment of Conservation Voltage Reduction Using Static Load Model Parameter in the Presence of Uncertainties

Higher penetration of solar PV and wind generation in distribution networks may change the conservation voltage reduction (CVR) capabilities. The uncertainties associated with the renewable generations and system loads are neglected in the deterministic CVR assessment. In this paper, a probabilistic framework for CVR assessment has been presented to assess the impact of uncertainties associated with renewable generations and system loads. A theoretical framework has been presented by establishing a mathematical relationship between the probability distribution of renewable generations (solar PV and wind) and the probability distribution of static exponential load model parameters. The simulation results have confirmed that the penetration of non-Gaussian solar PV and wind generation leads to non-Gaussian static exponential load model parameter distribution, which is validated by the normality tests (quantile-quantile plot, skewness, and kurtosis). Subsequently, the magnitude and probability distribution of the CVR capabilities of the network changes with the penetration of renewable generations, where the higher renewable penetration scenarios lead to higher CVR values and non-Gaussian (asymmetric) distribution.

Optimal Placement of Distributed Generation Units for Microgrid Planning in Distribution Networks

Due to increasing penetration of renewable distributed generation (DG), conventional distribution networks have been gradually transforming into their active form, where microgrids may serve as fundamental building blocks. As the primary step towards microgrid planning, optimal DGs placement and sizing can reduce the total energy losses by localizing power supply to loads. In this paper, a DG optimal placement method by minimizing the total energy losses is proposed, where the planning is formulated as a non-linear programming (NLP) problem. AC optimal power flow (OPF) is used to solve this planning problem by considering operational constraints and uncertainties in load and renewable power generation of the network. IEEE 33-bus test system and a real 404-bus distribution system operated by Saskatoon Light and Power in Saskatoon, Canada are used to validate the proposed method. The proposed method also shows superior performance compared to existing methods.

Tight power and energy coupling constraints of energy storage resources for unit commitment

AbstractEnergy Storage Resources (ESRs) can help promote high penetrations of renewable generation and shift the peak load. However, the increasing number of ESRs and their features different from conventional generators bring computational challenges to operations of wholesale electricity markets. In order to improve the computational efficiency, this paper tightens the generic ESR formulation for unit commitment. To avoid the complexity caused by ESR operations in both discharge and charge directions, a novel “decoupled analysis” is conducted to analyze one direction at a time. For each direction, ESRs over two and three time periods are categorized into several types based on their parameters. For each type, our recent four‐step systematic formulation tightening approach is used to construct the corresponding tight formulation. In order to consider more periods without analyzing all the drastically increased number of types, a series of major types are selected based on how many periods an ESR is able to discharge (charge) consecutively at the upper power limit. A related generic form of tight constraints over multiple periods is established. Moreover, validity and facet‐defining proofs of our tight constraints have been provided. Numerical testing results illustrate the tightening process and demonstrate computational benefits of the tightened formulations.

Online Optimization in Power Systems With High Penetration of Renewable Generation: Advances and Prospects

Traditionally, offline optimization of power systems is acceptable due to the largely predictable loads and reliable generation. The increasing penetration of fluctuating renewable generation and internet-of-things devices allowing for fine-grained controllability of loads have led to the diminishing applicability of offline optimization in the power systems domain, and have redirected attention to online optimization methods. However, online optimization is a broad topic that can be applied in and motivated by different settings, operated on different time scales, and built on different theoretical foundations. This paper reviews the various types of online optimization techniques used in the power systems domain and aims to make clear the distinction between the most common techniques used. In particular, we introduce and compare four distinct techniques used covering the breadth of online optimization techniques used in the power systems domain, i.e., optimization-guided dynamic control, feedback optimization for single-period problems, Lyapunov-based optimization, and online convex optimization techniques for multi-period problems. Lastly, we recommend some potential future directions for online optimization in the power systems domain.