High Penetration Of Renewable Generation Research Articles

A robust state estimation by optimal placement of measurement units considering loads/renewable generations and measurements uncertainty

AbstractThe supervisory control and data acquisition (SCADA) system is one of the key requirements for monitoring the power systems state. The power system state is estimated from the state estimation (SE) function, which uses the measurements results from different points. Inaccurate estimation may cause inefficient management of power systems and making decisions. Usually, the number of measurement units (MUs) is limited so the optimal location of MUs is very important in successful and accurate state estimation. The optimal placement of MUs gets more complicated when the actual state of the power system is uncertain due to the high penetration of renewable generation and load uncertainties. Considering the power system's actual state uncertainty and measurement units’ inherent error, this work presents a probabilistic model for the uncertainty of measuring based on the K‐medoids data clustering technique. Then the probabilistic optimal placement of MUs is solved by a binary particle swarm optimization (PSO) method. Considering the mentioned uncertainties makes the obtained solution robust against all the probable scenarios. The proposed technique is implemented on the IEEE 14‐bus test system and the results are discussed. Results show the effectiveness of the proposed method in improving the accuracy of power system state estimation.

Modeling forecast errors for microgrid operation using Gaussian process regression

Microgrids, denoting small-scale and self-sustaining grids, constitute a pivotal component in future power systems with a high penetration of renewable generators. The inherent uncertainty tied to renewable power generation, typified by photovoltaic and wind turbine systems, necessitates counterbalancing mechanisms. These mechanisms encompass Energy storage systems or conventional thermal fossil-fuel generators imbued with heightened flexibility. Addressing the uncertainty stemming from renewable generators mandates a cost-effective assessment and operational strategy for said compensatory devices. To this end, myriad uncertainty factors warrant scrutiny, conceivably concretized into a unified probability distribution function (PDF) that takes into account their temporal inter-dependencies. Diverse uncertainty factors, characterized by varying marginal distributions and scales, can be assimilated into a multivariate probability distribution through a conversion to normal distributions via rank correlation. However, with the escalation in the number of uncertainty factors embraced within a microgrid context, the endeavour becomes notably intricate when aiming to define conditional probability distributions originating from joint PDFs. This paper presents a method proposing the modelling of net-load forecast error distribution, considering the interplay among uncertainty factors. The approach introduces a data-driven Gaussian process regression technique for training and validating conditional PDFs among these uncertainty factors. Notably, this approach facilitates the transformation of said factors into normal distributions while preserving their inherent marginal characteristics. The resultant conditional density function, as per the proposed methodology, exhibits enhanced suitability for estimating net-load error distribution. Consequently, the conditional density function stemming from this proposed approach demonstrates superior aptitude in approximating the distribution of net load error.

Using the Thermal Inertia of Trains for Contributing to Primary and Supplementary Frequency Control in Grids With High Penetration of Renewable Generation

Using the Thermal Inertia of Trains for Contributing to Primary and Supplementary Frequency Control in Grids With High Penetration of Renewable Generation

Investigation of power quality issues in 14-bus electrical network with high penetration of renewable generation

Power quality issues arise in electrical networks when variable renewable energy (VRE) is integrated into them due to their random and intermittent nature which depends on weather conditions and other factors. The variation of solar irradiance throughout the day affects the energy produced by solar panels and the integration of solar power into electrical networks will result in changes and fluctuations in the voltage profile of buses. Reactive power compensation is required to improve the bus voltage levels of the electrical network to be within the required limits and the optimal allocation of reactive power compensation devices in the network is a complex problem to be investigated for the optimum injection of reactive power to obtain better voltage profiles for the entire network. This research investigated the penetration of variable solar energy into an electrical network in terms of voltage and reactive power flow. A variety of literature was reviewed in the scope of reactive power management in power systems and a gap in addressing the optimal allocation of compensation devices in the IEEE-14 bus was addressed based on the proposed methods followed by discussions of the results in terms of voltage profiles and reactive power flow in the buses. The objective is to produce an output power of higher quality and reliability for the loads so that intermittent sources of renewable energy can be more competent with energy sources such as fossil fuels that do not depend on weather conditions. Integration of methods using compensation optimisation (optimal allocation of capacitors) and volt-var regulation (smart inverter) to improve the voltage profile that was dropped and the fluctuations after penetration of solar power were carried out. A solar bus with variable energy generation was connected to the IEEE-14 bus to study the voltage variations. This was executed by the power flow calculation module to determine the voltages and reactive power in the buses of the network. With the optimum allocation of the capacitors, the voltage levels in all weak buses of the IEEE-14 bus were increased to be between 0.95 p.u. and 1.05 p.u. which was the voltage specifications of the Malaysian Grid Code Requirements. The voltage for every weak bus in the IEEE-14 bus showed a rise of 5.7% from 7 a.m. to 12 p.m. With that, the volt-var function was used for reactive power regulation at the point of common coupling (PCC) and a reduction of voltage deviation of 2.828 to 1.3% in the IEEE-14 bus was observed. The average voltage profile of all buses managed to attain a value of 98.99% from 95.673% (with solar power) with the optimal allocation of capacitors and volt-var regulation. The beneficiaries of this project will be the Sustainable Energy Development Authority (SEDA) which administers the Net Energy Metering (NEM) scheme and Tenaga Nasional Berhad (TNB) which is the Malaysian multinational electrical company focused primarily on the generation, transmission, and distribution of electricity in Peninsular Malaysia. The Energy Commission and Ministry of Energy and Natural Resources are also beneficiaries as they carried out a competitive bidding programme for large-scale solar (LSS) known as the LSS@MEnTARI or LSSPV4 to attain bids for the development of around 1000 MW AC of LSS power plants to be operational in Malaysia by 2022. This work will also be beneficial in future research in planning reactive power compensation devices in networks of multiple VRE sources, communication, and coordinated control of smart inverters, and incorporation of these devices for smart grid applications.

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.

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.

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.

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.

A Post-Event Generator Start-Up Strategy for Renewable Penetrated Transmission System Considering Dynamic Frequency Regulation

Restoring transmission systems (TSs) with a high penetration of renewable generation is a troublesome task. To enhance the resilience of these TSs, a deterministic post-event generator start-up strategy (GSUS) is proposed in this paper, where the conventional and renewable units are co-operated. However, the utilization of renewable generation yields a concern that the recovering TS may be exposed to the risk of frequency instability since the renewable units have the characteristics of low inertia and high uncertainty. Consequently, dynamic frequency regulation (DFR) is considered while a novel approximate method is proposed to linearize the non-convex DFR constraints. Besides, a novel uncertainty set associated with restoring states is established to model the nodal power fluctuations. On the basic of the above works, a robust GSUS with DFR is developed. To solve this GSUS efficiently, an adaptive robust-based methodology is proposed to explore the “worst” realization of the uncertainty and then the non-convex bi-level robust constraints are reformulated. Finally, the effectiveness of our works is verified on a real-world 129-bus TS.

An Efficient EMS for BESS in Monopolar DC Networks with High Penetration of Renewable Generation: A Convex Approximation

This research presents an efficient energy management system (EMS) for battery energy storage systems (BESS) connected to monopolar DC distribution networks which considers a high penetration of photovoltaic generation. The optimization model that expresses the EMS system with the BESS and renewable generation can be classified as a nonlinear programming (NLP) model. This study reformulates the NLP model as a recursive convex approximation (RCA) model. The proposed RCA model is developed by applying a linear approximation for the voltage magnitudes only at nodes that include constant power loads. The nodes with BESS and renewables are approximated through the relaxation of their voltage magnitude. Numerical results obtained in the monopolar version of a 33-bus system, which included three generators and three BESS, demonstrate the effectiveness of the RCA reformulation when compared to the solution of the exact NLP model via combinatorial optimization techniques. Additional simulations considering wind power and diesel generators allow one to verify the effectiveness of the proposed RCA in dealing with the efficient operation of distributed energy resources in monopolar DC networks via recursive convex programming.

Model predictive control based virtual synchronous generator for parallel-connected three-phase split-source converters in islanded AC Microgrids

Due to high penetration of renewable generation in power systems, and the need to provide the interface between distributed energy resources, the split-source inverter (SSI) provides both the boosting and the conversion capabilities in one single-stage. Also the need for converter-based artificial inertia has become more important. In this paper a model-predictive control (MPC) based on virtual synchronous generator (VSG) algorithm for a parallel-connected three-phase SSI is proposed for conceiving regulation of local voltage and realizing power-sharing of an islanded AC microgrid (MG). A virtual synchronous generator (VSG) is deployed to ensure active-power-sharing and provide inertia-emulation and hence reducing the rate of change of frequency (RoCoF) that results from sudden load change. To accomplish a simple control construction, quick dynamic performance, high stability, and enhanced current limitation, a finite-set MPC (FS-MPC) is used. The analysis and modeling of the proposed technique are presented in detail. A simulation model is used to investigate the proposed system performance.

Chance-Constrained OPF in Droop-Controlled Microgrids With Power Flow Routers

High penetration of renewable generation poses challenges to power system operation due to its uncertain nature. In droop-controlled microgrids, the voltage volatility induced by renewable uncertainties is aggravated by the high droop gains. This paper proposes a chance-constrained optimal power flow (CC-OPF) problem with power flow routers (PFRs) to better regulate the voltage profile in microgrids. PFR refers to a general type of network-side controller that brings more flexibility to the power network. Comparing with the normal CC-OPF that relies on node power flexibility only, the proposed model introduces a new dimension of control from power network to enhance system performance under renewable uncertainties. Adopting a partial linearization method and an iterative algorithm allows us to address the CC-OPF problem by iteratively solving a subproblem. Since the inclusion of PFRs complicates the subproblem and makes common solvers no longer applicable directly, a semidefinite programming relaxation is used to transform each subproblem into a convex form. The proposed method is verified on a modified IEEE 33-bus system and the results show that PFRs significantly reduce the standard deviations of voltage magnitudes and contribute to mitigating the voltage volatility, which makes the system operate in a more economic and secure way.

Probabilistic real-time grid operation management of future distribution grids with high penetration of renewable generators and electrical vehicles based on artificial intelligence

In this paper, we propose a novel method for real-time control of electric distribution grids with a limited number of measurements. The method copes with the changing grid behaviour caused by the increasing number of renewable energies and electric vehicles. Three AI based models are used. Firstly, a probabilistic forecasting estimates possible scenarios at unobserved grid nodes. Secondly, a state estimation is used to detect grid congestion. Finally, a grid control suggests multiple possible solutions for the detected problem. The best countermeasures are then detected by evaluating the systems stability for the next time-step.

Optimal Power Flow in DC Networks With Robust Feasibility and Stability Guarantees

With high penetrations of renewable generation and variable loads, there is significant uncertainty associated with power flows in dc networks such that stability and operational constraint satisfaction are of concern. Most existing dc network optimal power flow (DN-OPF) formulations assume exact knowledge of loading conditions and do not provide stability guarantees. In contrast, this article studies a DN-OPF formulation, which considers both stability and operational constraint satisfaction under uncertainty. The need to account for a range of uncertainty realizations in this article’s robust optimization formulation results in a challenging semi-infinite program (SIP). The proposed solution algorithm reformulates this SIP into a computationally tractable problem by constructing a tight convex inner approximation of the feasible region using sufficient conditions for the existence of a feasible and stable power flow solution. Optimal generator setpoints are obtained by optimizing over the proposed convex stability set. The validity and value of the proposed algorithm are demonstrated through various dc networks adapted from IEEE test cases.

Optimizing the physical design and layout of a resilient wind, solar, and storage hybrid power plant

As wind and solar technologies improve and their costs decrease, the share of power produced by these sources will increase. As the market penetration increases, these power sources will need to provide grid services, such as dispatchability, in addition to providing energy. One way to reduce variability, provide higher quality power to the grid, and address local grid stability issues is through colocating wind and solar power plants. In addition to operating reliably during normal operating conditions, in scenarios with high penetrations of renewable generation, it is important that these hybrid plants can withstand production disruptions and continue to supply power despite prolonged resource reduction, extreme weather events, or other disruptions. In this paper, we present a methodology to optimize a wind–solar-battery hybrid power plant down to the component level that is resilient against production disruptions and that can continually produce some minimum required power. We introduce the models and assumptions we used to simulate a hybrid power plant as well as the design variable parameterization and specific methods we used to optimize the plant. We demonstrate the performance of our method by comparing a plant optimized for different objectives, generation outage durations, minimum power requirements, and power purchase agreements. Although the plant design is sensitive to model parameters and various other assumptions, our results demonstrate some of the optimal designs that occur in different scenarios and what one should expect when designing a hybrid wind–solar-storage power plant.

Assessment of the influence of demand-side responses on high-proportion renewable energy system: An evidence of Qinghai, China

The development of renewable energy (RE) is a critical path to achieve global climate goals and energy conversion. The understanding of Demand-side response (DR) is crucial for the stability and efficiency of renewable energy system (RES). The DR strategies are performed by coordinating various energy sources and electricity demand optimally in the operation of high penetration of renewable generations across different seasons from 2015 to 2050 using Qinghai, China as an example. The results show the reduced peak demand by DR is various in different seasons, and the shifted peak demand will be 56.8 GWh-596.5 GWh in 2050. In general, DR could cause a significant reduction of the total costs by avoiding unnecessary installed capacity from ￥2.19 billion to ￥28.62 billion in 2050. In the short-term, the structure of high-proportion RES cannot rapidly adjust to a new form. However, in the long-term, hydropower will have a large share in RES due to the ability of improving the synergistic stability of the high-proportion RES. In addition, the adoption of DR demonstrates a great potential on reducing PV curtailment and wind curtailment.

Transmission System Electromechanical Stability Analysis with High Penetration of Renewable Generation and Battery Energy Storage System Application

Despite the benefits of wind and solar photovoltaic generation, its stochastic characteristic imposes uncertainties on the electric power system’s transient stability. The dynamics considering large synchronous generators has been studied for many decades, and its behavior is well known. On the other hand, the penetration of renewable sources has reached records, showing that it is still vital to study their impact. The present work proposes computer modeling and simulations for the dynamic analysis of electromechanical stability in a transmission system with significant renewable generation. In general, the literature does not propose solutions to the electromechanical stability analysis, proving that there are gaps to be filled. Therefore, the main work contribution consists of designing and coupling a battery energy storage system to a solar plant to smooth power variations. A significant innovation is the proposition of different scenarios that replicate disturbance situations, where the analyses were carried out using the Brazilian grid code. It was possible to evaluate the robustness of the proposed system and the efficiency of the storage system in mitigating the impacts of renewable generation. Thus, it is possible to achieve high levels of renewable penetration if extensive and rigorous studies are carried out.

Solid-State Transformer-Based DC Power Distribution Network for Shipboard Applications

Due to simplicity, efficiency, and the ability to accommodate energy storage devices, DC distribution networks have been seen as an optimal alternative to AC distribution networks, especially aboard future electric ships. The emerging distribution DC system entails new control and management techniques. Therefore, an integrated DC power distribution network aboard an electric ship is selected as the case study in this paper. To meet the requirements of such a large-scale mobile power system, a multiport solid-state transformer (SST) based on silicon carbide (SiC) switches/MOSFETs is proposed. Thus, the system embodiment can significantly be reduced. Moreover, at the DC distribution level, a high penetration of renewable generation with energy storage is allowed and a six-phase asymmetrical induction machine (IM) can directly be integrated. Simulations have been conducted based on a 2 MW shipboard distribution network. The effects of the propulsion system dynamics on the SST are highlighted as well. Finally, a 2 kW lab-scale prototype has been implemented to validate the theoretical findings.

Temporal aggregation for large‐scale multi‐area power system models

The study of integrated electricity systems that consist of several interconnected areas in the long term often results in large-scale complex models, that are difficult to solve. The already large spatial size of these systems, combined with a fine-grained time representation, necessary to capture the short-term variability arising from the high penetration of renewable generation, increases the complexity of the problem, and thus, its computational cost. To overcome this issue, temporal reduction techniques are generally applied. However, the application of time aggregation in interconnected systems represents a challenge. The goal is to select the best possible time aggregation that considers at the same time the particularities of each of the areas that make up the whole system. To do so, the authors propose a new methodology for temporal aggregation in multi-area energy system models. By implementing a multi-dimensional clustering algorithm, the original hourly data is transformed into system states, or group of hours that share similar characteristics, reducing significantly the computational burden required to solve it. Together, an accurate representation of the variability of the system is achieved. The main conclusions are derived from a real-size case study based on the electricity markets of three European countries. The sensitivity analysis performed shows the degree of accuracy of the results obtained, as well as the computing cost incurred for different temporal configurations. Ultimately, the results show the benefits of using this methodology over a more conventional approach.