Renewable Generation Resources Research Articles

Chance-constrained co-optimization of peer-to-peer energy trading and distribution network operations

Peer-to-Peer (P2P) energy markets enable participants to trade locally produced electrical energy at a mutually agreed-upon price. Distributed Energy Resources (DERs) and flexible loads are fundamental components of P2P energy markets, facilitating the market participants to supply or consume energy in accordance with trading agreements. Consequently, it is vital to account for the stochastic nature of DERs and load demand while designing the P2P energy trading model in order to attain optimal operation of P2P energy markets and secure operation of the distribution network. The scenario-based and robust optimization techniques commonly used to characterize uncertainty suffer from data requirement, computational burden, and solution conservatism standpoints. This paper, therefore, proposes a chance-constrained model for P2P energy trading considering renewable energy uncertainty and distribution network constraints. The uncertain behaviour of renewable generation resources is modelled using chance constraints and integrated with the decision-making process of the P2P energy market and distribution network operation. The chance-constrained P2P energy trading problem is reformulated as a deterministic equivalent problem that can be solved by readily available commercial solvers. The ex-ante and ex-post-performance of the proposed chance-constrained formulation is evaluated on a 15-bus radial distribution network, while the scalability of the proposed formulation is demonstrated on modified IEEE 33-bus and 69-bus test networks. The results showcase the effectiveness of the proposed chance-constrained model in handling the uncertainty and respecting the network constraints while deriving the P2P energy trading outcomes.

Capacidad de alojamiento y dimensionamiento óptimo: caso de estudio colombiano

The global energy transition has driven the adoption of renewable generation resources in distribution networks worldwide. This shift has presented new operational challenges for these networks. One such challenge involves determining the ideal sizing of these resources and the hosting capacity of the distribution networks to maximize their potential to enhance technical parameters within the system. This article proposes strategies to optimize the generation capacity and the selection of nodes in Colombian distribution networks, prioritizing nodes of lower voltage to improve reliability and continuity of supply. Specific methodologies are developed due to the passive and radial nature of these networks.

Performance and dynamics of California offshore wind alongside Western US onshore wind and solar power

We use statistical methods to evaluate the coincidence, variability, alignment, and predictability of California offshore wind power production with electricity demand and onshore renewable resources in the United States Western Interconnection region. We consider previously reported benefits, and evaluate possible advantages of deploying offshore wind as compared to onshore wind or solar power generation, such as coincidence with peak power demand and complementarity with other renewable power resources. Additionally, we use the Fast Fourier Transform and linear regression to assess the predictability of power with onshore wind and solar. The Pearson correlation reveals that onshore wind and solar are the most complementary renewable power generation resources, while there is no consistent correlation or anti-correlation between the offshore wind sites and the demand, solar, or onshore wind. Using a demand-based value metric, we find that the six potential offshore wind sites are more valuable than all other renewable resources during peak hours in the summer in California. Finally, the Fourier analysis reveals that offshore wind may be a highly variable resource.

Energy Demand Management in an Industrial Manufacturing Plant using MPC and Neurofuzzy Models

An MPC controller is proposed to maximise the use of renewable energy in a manufacturing process. The strategy has been applied in a manufacturing system which has several machines, renewable generation resources, a combined heat and power (CHP) generator for power production, and a battery bank for energy storage. The work aims to maximise the use of renewable energy sources in this process, also taking into account the price of the electricity market, to reduce the cost. The use of neurofuzzy models for the prediction of the energy produced by renewable generators allows a dynamic prediction, using input values obtained from typical forecasting variables (wind speed, global irradiance, etc.).

A MIQP Approach Based on Demand Response in Distribution Networks to Improve the Multi-Objective Function in the Presence of Renewable Energy Resources and Batteries to the Subway Systems

The increasing expansion of devices such as electric vehicle charging stations, renewable energy resources, and electric subways, will cause an imbalance between load and production in the distribution network, which will lead to an increase in power losses, a decrease or increase in voltage and ultimately impose more costs on the independent system operator (ISO). In this paper, we have presented a mixed integer quadratic programming (MIQP) optimization model to improve the distribution network performance in the presence of these devices. The proposed model is a mixed integer model including demand side management modeling, energy storage system, battery to subway (B2S) system, optimal control of on-load tap changer (OLTC), step voltage regulator (SVR), fossil generation resources, renewable energy and capacitors, and shunt reactors. The considered multi-objective function is a scenario-based stochastic model, which accurately models the uncertainties in renewable energy resources. Paying attention to the nature of the proposed model will guarantee globally optimal solutions. The proposed model runs on the standard network of 33 buses, and the simulation results guarantee the optimal and accurate performance of the proposed model. The simulation results demonstrate that the outage of three distributed generation units from the network causes an average increase of 55% in energy losses, and the outage of three renewable units leads to an increase in emissions of about 40%. Another point received from the results illustrated that with the implementation of the proposed model under any load level of the network, as well as the outage of fossil and renewable distributed generation (DG) resources, no load shedding will occur in the network during the 24 hours. Finally, due to the short execution run time, the proposed model can be used for real and online grids.

A New Decentralized PQ Control for Parallel Inverters in Grid-Tied Microgrids Propelled by SMC-Based Buck–Boost Converters

Nowadays, the microgrid (MG) concept is regarded as an efficient approach to incorporating renewable generation resources into distribution networks. However, managing power flows to distribute load power among distribution generators (DGs) remains a critical focus, particularly during peak demand. The purpose of this paper is to control the adopted grid-tied MG performance and manage the power flow from/to the parallel DGs and the main grid using discrete-time active/reactive power (PQ) control based on digital proportional resonant (PR) controllers. The PR controller is used to eliminate harmonics by acting as a digital infinite-impulse response (IIR) filter with a high gain at the resonant frequency. Additionally, the applied PR controller has fast reference signal tracking, responsiveness to grid frequency drift, and no steady-state error. Moreover, this paper describes the application of robust nonlinear sliding mode control (SMC)-technique-based buck–boost (BB) converters. The sliding adaptive control scheme is applied to prevent the output voltage error that occurs during DG failure, load variations, or system parameter changes. This paper deals with two distinct case studies. The first one focuses on applying the proposed control for two parallel DGs with and without load-changing conditions. In the latter case, the MG is expanded to include five DGs (with and without DG failure). The proposed control technique has been compared with the droop control and model predictive control (MPC) techniques. As demonstrated by the simulation results in MATLAB software, the proposed method outperformed the others in terms of both performance analysis and the ability to properly share power between parallel DGs and the utility grid.

New Tilt Fractional-Order Integral Derivative with Fractional Filter (TFOIDFF) Controller with Artificial Hummingbird Optimizer for LFC in Renewable Energy Power Grids

Recent advancements in renewable generation resources and their vast implementation in power sectors have posed serious challenges regarding their operation, protection, and control. Maintaining operating frequency at its nominal value and reducing tie-line power deviations represent crucial factors for these advancements due to continuous reduction of power system inertia. In this paper, a new modified load frequency controller (LFC) method is proposed based on fractional calculus combinations. The tilt fractional-order integral-derivative with fractional-filter (TFOIDFF) is proposed in this paper for LFC applications. The proposed TFOIDFF controller combines the benefits of tilt, FOPID, and fractional filter regulators. Furthermore, a new application is introduced based on the recently presented artificial hummingbird optimizer algorithm (AHA) for simultaneous optimization of the proposed TFOIDFF parameters in the studied two-area power grids. The contribution of electric vehicle (EVs) is considered in the centralized control strategy using the proposed TFOIDFF controller. The performance of the proposed TFOIDFF controller has been compared with the existing tilt with filter, PID with filter, FOPID with filter and hybrid fractional-order with filter LFCs from the literature. Moreover, the AHA optimizer results are compared with the featured LFC optimization algorithms in the literature. The proposed TFOIDFF and AHA optimizer are validated against renewable energy fluctuations, load stepping, generation/loading uncertainty, and power-grid parameter uncertainty. The AHA optimizer is compared with the widely-used optimizers in the literature, including the PSO, ABC, BOA, and AEO optimizers at the IAE, ISE, ITAE, and ITSE objectives. For instance, the proposed AHA method has a minimized IAE after 34 iterations of 0.03178 compared to 0.03896 with PSO, 0.04548 with AEO, 0.04812 with BOA, and 0.05483 with ABC optimizer. Therefore, fast and better minimization of objective functions are achieved using the proposed AHA method.

Feedback control approaches for restoration of power grids from blackouts

The automated restoration of power systems with variable energy resources is a timely problem to tackle. Automated restoration advice can support operators in deciding on strategic actions to restore power grids from a blackout with a mix of conventional and renewable generation resources. To this end, this paper frames the restoration process of power grids with solar resources as a nonlinear dynamic model with algebraic constraints in discrete time which is steered by feedback control loops. We discuss two feedback-control strategies based on greedy and reinforcement learning algorithms, and contrast their performance with restoration plans generated by a mixed-integer linear program. We found that the reinforcement learning algorithm infers restoration actions faster than the greedy one. However, the tuning process of the reinforcement learning parameters is slower than for the greedy one.

Comparative life cycle environmental impact assessment of renewable electricity generation systems: A practical approach towards Europe, North America and Oceania

The choice of a power plant in different nations depends on the required resources availability, abundance, and reliability. It is evident that renewable energy generation resources provide a more sustainable solution than the fossil fuels. However, as a complete system, renewable energy generation systems also have some overall environmental impacts on humankind and ecosystems. The main objective of this paper is to addresses the environmental effects caused by different types of renewable energy generation systems through process-based life-cycle impact analysis. A comparative study is carried out among different renewable energy generation technologies which include wind, photovoltaic, biomass, and hydro power. Life-cycle impact analysis has been carried out by Institute of Environmental Sciences (CML), Eco-indicator 99, Cumulative Energy Demand (CED) and Ecopoints 97 approaches. The superiority of this work is creating a comprehensive life-cycle inventory following a reliable global database, assessing the impacts for about 10 midpoint impact categories and three endpoint indicators, and taking account of the fossil-fuel-based energy consumption rate of each plant. The key findings reveal that photovoltaic power plants have the highest environmental impacts in the types of ozone layer depletion, fresh water aquatic ecotoxicity and marine aquatic ecotoxicity, while biomass plants impact on the categories of abiotic depletion, global warming, photochemical oxidation, acidification, and eutrophication. Moreover, the wind power plants have the more significant environmental effect on human toxicity and terrestrial ecotoxicity types. Overall, hydropower plants are found to be much more environment-friendly than other renewable electricity generation systems.

Real Time Scheduling of a Microgrid Equipped with Ultra-Fast Charging Stations

Ultra-fast charging infrastructures are gaining increasing interest thanks to their ability to reduce the charging-time of plug-in electric vehicles to values comparable to those of the refueling of traditional vehicles in gas stations. This is a consequence of the increasing rated power of both on-board batteries and charging equipment. On the other hand, the increased values of charging power have led to an increased impact on the power distribution networks, particularly in terms of line currents and bus voltages. In presence of large penetration of ultra-fast charging devices, in fact, both currents and voltages are affected by larger variations whose values can exceed the admissible limits imposed by the technical constraints and by the levels of quality of service. In order to reduce the impact of this typology of vehicles’ charging on the electrical infrastructure, in this paper a methodology is presented which allows managing a microgrid in presence of ultra-fast charging stations by satisfying the constraints of the grid, while preserving the expected short charging-time for electric vehicles. To this end, a proper optimal strategy is proposed which coordinates the demands of electric vehicles and of the other loads of the microgrid with the power provided by the renewable energy generation resources. The proposed approach aims to optimally control the active and reactive power of charging stations and renewable generation units and to minimize the charging time of a fleet of plug-in electric vehicles while satisfying the constraints on the technical aspects and on the quality of service. The proposed approach has been tested on a test system and the results, proposed in the last part of the paper, demonstrate the feasibility of the proposed approach.

A Hybrid Deep Learning‐Based Network for Photovoltaic Power Forecasting

For efficient energy distribution, microgrids (MG) provide significant assistance to main grids and act as a bridge between the power generation and consumption. Renewable energy generation resources, particularly photovoltaics (PVs), are considered as a clean source of energy but are highly complex, volatile, and intermittent in nature making their forecasting challenging. Thus, a reliable, optimized, and a robust forecasting method deployed at MG objectifies these challenges by providing accurate renewable energy production forecasting and establishing a precise power generation and consumption matching at MG. Furthermore, it ensures effective planning, operation, and acquisition from the main grid in the case of superior or inferior amounts of energy, respectively. Therefore, in this work, we develop an end‐to‐end hybrid network for automatic PV power forecasting, comprising three basic steps. Firstly, data preprocessing is performed to normalize, remove the outliers, and deal with the missing values prominently. Next, the temporal features are extracted using deep sequential modelling schemes, followed by the extraction of spatial features via convolutional neural networks. These features are then fed to fully connected layers for optimal PV power forecasting. In the third step, the proposed model is evaluated on publicly available PV power generation datasets, where its performance reveals lower error rates when compared to state‐of‐the‐art methods.

Robust management and optimization strategy of energy hub based on uncertainties probability modelling in the presence of demand response programs

The energy hub is considered one of the most important parts of the multi-carrier energy distribution systems. One of the major challenges facing the energy hub idea is the optimal distribution and management of energy based on renewable and non-renewable power generation resources in the presence of demand response programs and uncertainties. This study aims to present an optimal management and distribution model of energy based on uncertainties of probability modelling of renewable energy resources, uncertainties of electrical loads (electric vehicles (EVs)), operating and maintenance cost, cost modelling of greenhouse gas emissions, and electric and thermal demand response programs. Therefore, the proposed model is presented based on a robust optimization model and mixed-integer linear programming. Uncertainties in this research are divided into two categories; technical and economic. The technical part includes the uncertainties caused by renewable energy resources and EVs, simulated based on the Monte Carlo method and their probability distribution function. The economic part includes modelling the uncertainty caused by the price of electricity. The operation model and the objective function of the optimization problem are modelled using the demand response models based on peak shaving and load shifting techniques.

Impact of Societal Events on Frequency Stability Considering LED TVs in Low Inertia Trending Power Systems

Due to environmental concerns, renewable energy sources (RESs) gradually replace conventional power generations. Over the past decade, wind and solar electricity generation have increased from 55 to 275 million MW·h and from 2 to 96 million MW·h, respectively, in the U.S. Recently, FERC order 2222 allows distributed energy resources, including distributed renewable generation and energy storage, to participate in the electricity wholesale market through aggregations. Limited rotating inertia of renewable generation resources results in reduced system inertia, which will increase the rate of change of frequency due to sudden mismatches of generation and demand. During the high visibility national social event, the brightness of LED TV display put simultaneous demand change in power systems. With the development of slim, tightly packaged, and yearly update features of smart TV, the penetration of different LED displays climbed rapidly from 9% to 60% in the U.S. during 2012–2019. In this article, the impact of societal events on frequency stability caused by large viewership on LED TVs are discussed. Increasing RESs penetration and FERC order 2222 are considered in this study. The power demand data of a 55-inch LED TV are collected and analyzed to address the problem. This article discusses the frequency stability issue caused by LED TVs in a low inertia power system. It can help system operators to develop frequency control practices accordingly.

Role of policy in the development of business models for battery storage deployment: Hawaii case study

Hawaii's 100% Renewable Portfolio Standard target has led to rapid growth in renewable generation resources. This has resulted in grid issues given the islanded nature of Hawaii's grid. The state has decided to use battery storage in its grid to address these issues. This motivates us to try to understand the various business models in the state and the minimum set of barriers that must be overcome for its successful deployment. We achieve the former by assessing the projects in the state and identifying the dominant business models. For the latter, we use a barrier-solution framework that posits the minimum set of barriers that must be met for the successful deployment of battery storage business models and we verify it by identifying the role of various policies/drivers in supporting these business models. As a result, we find predominantly three business models in Hawaii: Co-located Front-of-the-Meter, Behind-the-Meter, and Aggregated Behind-the-meter. We also verify that at least one barrier from barrier categories associated with market demand and project economics along with all the barriers related to interconnection and market participation must be addressed for successful deployment of battery storage. Based on these findings, we give recommendations to policymakers seeking to develop a battery storage market.

Flexibility and real options analysis in power system generation expansion planning under uncertainty

Over many years, there has been a drive in the electricity industry towards better integration of environmentally friendly and renewable generation resources for power systems. Such resources show highly variable availability, impacting the design and performance of power systems. In this article, we propose using a stochastic programming approach to optimize Generation Expansion Planning (GEP), with explicit consideration of generator output capacity uncertainty. Flexibility implementation - via real options exercised in response to uncertainty realizations - is considered as an important design approach to the GEP problem. It more effectively captures upside opportunities, while reducing exposure to downside risks. A decision-rule-based approach to real options modeling is used, combining conditional-go and finite adaptability principles. The solutions provide decision makers with easy-to-use guidelines with threshold values from which to exercise the options in operations. To demonstrate application of the proposed methodologies and decision rules, a case study situated in the Midwest United States is used. The case study demonstrates how to quantify the value of flexibility, and showcases the usefulness of the proposed approach.

Carbon capture: Prospects and policy agenda for CO2-neutral power generation

By mid-century, global mean temperature increase from pre-industrial levels must remain below 1.5 °C to resist the forces of climate chaos. Recent studies emphasize the central role that the electricity system must play in achieving 100 % carbon-free generation, particularly through greater reliance on zero-carbon, firm output. Major firm power options that emit little or no carbon include hydro, nuclear, geothermal, and carbon capture and storage (CCS). This article examines the status of CCS, now applied at coal-fired power plants and under consideration at natural gas plants in North America and certain European nations. We identify key developments: (i) CCS can eliminate and permanently store virtually all fossil-fired CO2 emissions from power plants; (ii) following targeted policies, doing so would be cost-competitive with other strategies to generate zero-carbon, firm electricity; and (iii) combining aggressive upstream greenhouse gas emissions mitigation with near-100 % carbon capture at the power plant can create significant benefits on par with the lifecycle emissions of other renewable and clean generation resources. Finally, we examine the policy pathways, infrastructure, and jurisdictions central to CCS expansion—particularly in the U.S. and its subdivisions. Also discussed is the critical need to export CCS technology to all parts of the globe, especially areas like China and Southeast Asia that are likely to depend on fossil electricity for decades.

Data driven day-ahead electrical load forecasting through repeated wavelet transform assisted SVM model

Electrical load forecasting is an integral tool used by the grid operator to operate the smart power network. The information related to the electrical load is a prerequisite towards the effective and optimal operation of the power network with renewable and conventional generation resources. Economical bidding in the energy markets is directing research towards a superior forecasting model. Statistical and machine learning models have been used for electrical load forecasting while considering the electrical load as a time-series signal. This paper proposes a hybrid model that combines the Wavelet Transform (WT) and Support Vector Machine (SVM) features in estimating a regression model for electrical load forecasting utilizing the historical time-series information of electrical load. The WT decomposes the electrical load time-series data into various sub-series. The error contribution in forecasting due to the individual sub-series is estimated using Mean Absolute Error (MAE) in forecasting for each sub-series. The proposed Repeated WT-based SVM model (RWT-SVM) selects the sub-series with the highest MAE for further decomposition through WT. This results in a better forecasting model for the sub-series with the highest MAE, thereby improving the overall forecasting ability of the RWT-SVM model. The superiority of the proposed Repeated WT-based SVM model (RWT-SVM) for electrical load forecasting is justified using various data sets and comparing with some of the existing forecasting models.

A data-driven electric water heater scheduling and control system

Domestic hot water (DHW) heating accounts for up to 30% of average household energy use. Compared to gas fired water heaters, electric water heaters (EWH) can be powered by renewable generation resources, thus making it a potential renewable heating option. Furthermore, with the growing need for energy storage, incorporation of renewable resources, and initiatives worldwide, the electrification of DHW heating is expected to continue the rapid growth. However, many commercial EWH products with monitoring and alerting functionalities lack the intelligence to optimize and perform predictive control with data; on the other hand, research studies with refined models and simulations come short in incorporating real-time data and providing robust optimal controls under uncertainties in real-world settings. This paper presents a EWH Smart Scheduling and Control System using data-driven disturbance forecasts in a robust Model Predictive Control (MPC) to accomplish various demand side management objectives. Testing with a real-world EWH dataset and a two-state EWH model, robust MPC simulations are conducted on a central EWH supplying DHW for a multi-unit apartment building with quantified prediction uncertainty. Results show that the proposed system is capable of anticipating DHW demand with an uncertainty interval covering up to 97% of the actual demand during the test days and reducing electricity cost up to 33.2% as well as maintaining a desired DHW temperature without affecting user comfort. Further, the flexibility of the system to alter load profiles under different Demand Response (DR) programs is demonstrated. Reductions in both power and water consumption can be accomplished. The proposed system can create an implementable solution of forecasting DHW usage and optimizing controls as a part of a robust and reliable building energy management and control system in real-world settings.

Optimal frequency response of VPP-based power systems considering participation coefficient

The remarkable presence of renewable resources and the consequent development of virtual power plants (VPPs) in interconnected networks can reduce inertia and spinning reserve in power systems. This issue has created a challenge for power system engineers and analysts to maintain frequency stability in VPPs. Based on the system frequency response (SFR) model and the principal equations of VPPs, this paper presents a new model for the frequency response of a VPP-based interconnected power system. By presenting a method, the optimal reserve amount of generation units of VPP is determined in order to obtain the best frequency response in the frequency recovery for deal with system disturbances. Finally, to analyze the frequency response of the system, various scenarios of the presence or absences of each new resource and its combination with the conventional power networks are simulated. The simulation results indicated the need for the participation of energy storage systems (ESS) and renewable generation resources to safeguard the network frequency stability.

Mitigation of transient overvoltages in microgrid including PV arrays

This study investigates the mitigation of transient overvoltages (TOVs) in a microgrid supplied by renewable distributed generation resources, which include photovoltaic (PV) generators, hydro generation unit, and wind power generators. ATP/EMTP is used in this study as electromagnetic transient software. Accurate simulation models are needed to carry out the study. Therefore, a three-phase PV system model based on the equivalent Newton–Raphson circuit is used, and the efficiency of the PV system components is studied. Then, the use of the PV generation system model for switching studies is investigated using two different electrical systems. The use of different mitigation methods for TOVs at the microgrid is investigated. Three different mitigation methods are considered. First, a device to compensate reactive power such as static var compensator (SVC) is represented as a TOVs mitigation method. Then, adding a supercapacitor as TOVs mitigation method is evaluated. After that, a combination of active and reactive power simultaneously to mitigate TOV, by the addition of SVC to a supercapacitor, is investigated. The grid-connected mode and isolated mode of the microgrid are considered. The results show that the usage of SVC with supercapacitor has the ability to mitigate the transient overvoltage and the waveforms are more regular.