Integration Of Renewable Energy Generation Research Articles

Application of deep reinforcement learning in electricity demand response market: Demand response decision-making of load aggregator

With the large-scale integration of renewable energy generation, developing the potential of demand response is becoming more and more significant. However, the number of consumers is huge, the electricity consumption behaviors are various and the information of consumers is incomplete, thus there are great difficulties in modeling and processing demand response by the conventional mathematical methods. Therefore, how to accurately predict the response behaviors of consumers and select the appropriate consumers for demand response is worthy of in-depth discussion. In this paper, the decision-making method of the load aggregators who participant in demand response market on behalf of the small and medium-sized consumers based on Deep Q-network algorithm is proposed, supporting the aggregators to properly guide the potential demand response consumers. At the same time, a dynamic consumer classification method based on self-organizing maps algorithm is also proposed, supporting the aggregators to accurately predict the response behaviors of the consumers. Simulation results show that the proposed method can effectively realize the classification of the consumers and result in a more beneficial demand response.•The proposed method does not require complete information, such as demand response behavior parameters of the consumers.•Self-organizing maps can realize a dynamic classification of demand response consumers as the case may be.•DQN algorithm can effectively realize the demand response decision-making of aggregators under the condition of incomplete information.

Research on harmonic power flow calculation of novel urban distribution network considering the switching power supply harmonic and the correlation of impedance-frequency

Harmonic power flow calculation is the basis of harmonic analysis, tracing, and treatment. This paper focuses on the harmonic issues associated with the implementation of the “dual-carbon” plan in novel urban distribution networks. Due to the widespread integration of renewable energy generation into urban electrical grids, an increasing number of power electronic devices are being connected to the grid, which has a certain impact on harmonic power flow. Firstly, the harmonic components of 6-pulse and 12-pulse rectifiers are analyzed for the switching power supply model widely used in the distribution network. Secondly, the equivalent parameter models of the generator, the transformer, and the lines are reviewed. Finally, from the perspective of power balance, a harmonic power flow model considering the terminal switching power supplies and the correlation between impedance and frequency is proposed. Simulation verification is conducted to demonstrate the feasibility and accuracy of the proposed method.

Robust Collaborative Scheduling Strategy for Multi-Microgrids of Renewable Energy Based on a Non-Cooperative Game and Profit Allocation Mechanism

The Multiple Microgrid System (MMG) facilitates synergistic complementarity among various energy sources, reduces carbon emissions, and promotes the integration of renewable energy generation. In this context, we propose a two-stage robust cooperative scheduling model for MMGs based on non-cooperative game theory and a benefit allocation mechanism. In the first stage, considering electricity price fluctuations and uncertainties in wind and solar power outputs, a robust optimization approach is applied to establish an electric energy management model for MMGs. This model enables point-to-point energy sharing among microgrids. In the second stage, addressing the benefit allocation problem for shared electric energy, we introduce a Cost Reduction Ratio Distribution (CRRD) model based on non-cooperative game theory. The generalized Nash equilibrium is utilized to determine the benefit distribution for shared electric energy. Finally, through case studies, the proposed model is validated, ensuring fair returns for each microgrid. The results indicate that the proposed model optimizes the operational states of individual microgrids, reduces operational costs for each microgrid, and lowers the overall total operational costs of the MMG system. Additionally, an investigation is conducted into the impact of electricity price uncertainty coefficients and confidence levels of wind and solar uncertainties on the operational costs of microgrids.

Tool for evaluation of energy system options for municipalities

To reach the goal of net-zero emissions in 2050, posed by the Swiss energy strategy, our society faces major challenges for the integration of renewable energy generation and distribution, and changes in the energy system are inevitable. The development of decentralised renewable energy sources and the necessary extensions of the power grid are only one aspect. A high percentage of the energy consumption in the residential sector consists of space heating and cooling of buildings. Therefore, the changes will also affect the thermal energy sector, e.g. through renovations of buildings, replacements of fossil heating systems with heat pumps or connections to district heating networks. To support the planning involved in the changes of the energy system, we provide a tool for visualisations of energy system options and scenarios and recommendations for optimisations based on publicly available data, modelling demands and simulating energy systems in a mixed-integer linear programming framework. We present a first version of the recommender tool which focuses on the support of municipality representatives for drawing up their communal energy strategies.

Distributionally robust hierarchical multi-objective voltage risk management for unbalanced distribution systems

The large-scale integration of renewable energy generators (REGs) is key to reducing carbon emissions. However, the intermittency of REG outputs poses operational risk challenges on distribution systems (DSs). This paper proposes a novel Wasserstein-based distributionally robust assessment for the voltage risk index that represents both likelihood and non-linearly increasing severity of voltage violations. We propose a hierarchical DRO model for risk management that integrates a multi-objective distributionally robust optimization (DRO) at the distribution system operator (DSO) level and a basic DRO at the user level. Then, a systematic solution method is developed, where the proposed model is equivalently reformulated into tractable bilevel mixed-integer convex programs. The effectiveness, advantages, and scalability of the proposed method are validated on the IEEE 33-bus system and unbalanced IEEE-123-bus system.

The VSC-HVDC Transmission System Performance Assessment

As the use of renewable resources increases the transfer of power from remote area to consumer is a big challenge. Therefore, Voltage Source Converter High Voltage Direct Current (VSC-HVDC) transmission is gaining more popularity for integration of renewable energy generators to the consumers using smart grid. Even after many advantages of HVDC transmission system it is not used popularly due to unexpected instability in the system. However recent developments in semiconductor devices and control techniques the HVDC link operation in transmission system becomes easier. In this paper VSC based HVDC system is designed with AC-DC converter model for power flow assessment of both sides of HVDC link. To analyze the proposed model a simulation model of 75km DC line with voltage source converter (VSC) based AC-DC converter model on both side of DC line is designed in a MATLAB environment. HVDC link efficiency is calculated by analyzing power flow through AC lines at both ends of HVDC line.

Determining optimal generator start-up sequence in bulk power system restoration considering uncertainties: A confidence gap decision theory based robust optimization approach

A reasonable start-up sequence of generators considering renewable energy generation integration can effectively improve the restoration efficiency of the power system concerned. The restoration strategy of generators is time-sensitive, however, the computation procedure of existing methods is not fast enough to meet the requirements of practical applications. In this paper, the generator start-up sequence optimization (GSSO) problem is addressed with the integration of wind-solar hybrid systems (WSSs) considered in bulk power systems. Specifically, a deterministic model for GSSO is formulated to maximize the total generation capability during the restoration procedure, in which fewer auxiliary variables are required to promote the computational efficiency. On this basis, in order to deal with the inherent and significant uncertainties of WSSs, a confidence gap decision theory (CGDT) based approach is proposed combining the advantages of stochastic optimization and the information gap decision theory based method while considering the worst-case realization of unknown probability distributions, so that a chance-constrained robust generator start-up solution is thereby attained. Moreover, the formulated CGDT based model is transformed into a mixed integer linear programming problem. Finally, the IEEE 39-bus power system and a modified version of an actual power system in China are employed to demonstrate the feasibility and efficiency of the proposed method.

Voltage profile improvement and power loss minimization of new haven injection substation for optimal placement of DG using harmony search algorithm optimization technique

The main importance of a power grid is to transfer electrical energy from the production to the consumption, while maintaining an acceptable reliability and voltage quality for all customers. The role of electrical power distributions to have centralized plants distributing electricity through Distributed Generation (DG) which reduces the Power Loss on transmission and distribution station and also improve voltage profile by optimization. This research work analyzes New Haven injection substation for optimal placement of DG generation based on wind power renewable energy source to the Distribution network and voltage stability using Harmony Search Algorithm. The model developed was simulated using MATLAB/SIMULINK software. The work applied new improved Harmony Search Algorithm for integration of renewable energy generation in the distribution network with positive impact on the operation of the voltage stability between 0.95 to 1.05p.u and reactive power distribution improvement through minimization of line losses. The total power system Loss without and with DG are 338.712KW and 307.9KW. Also, the voltage profiles of the sixteen faulty buses are stabilized for efficient performance of substation. Hence, the percentage of power system improvement is 27.23%. Therefore from the results, there is reduction of power Loss when DG is applied and optimised in the power system.

Power System Flexibility Enhancement with Energy Storages Considering Charging Characteristics

With the increasing integration of renewable energy generations, the renewables’ uncertainties impact the power system operation. So, it is critical to enhancing the system’s operational flexibility. To this end, one of the most useful solutions is to deploy energy storage. This paper proposes a power system flexibility enhancement method using energy storage systems (ESSs). The grid’s flexibility requirement is analyzed, and the flexibility enhancement model is constructed, considering the ESSs’ charging and discharging characteristics. Case studies on an IEEE 33-node system verify the quantitative method. The conclusions indicate the ESSs could efficiently meet the system’s flexibility requirements.

Electron and Ion Transport in Lithium and Lithium-Ion Battery Negative and Positive Composite Electrodes.

Electrochemical energy storage systems, specifically lithium and lithium-ion batteries, are ubiquitous in contemporary society with the widespread deployment of portable electronic devices. Emerging storage applications such as integration of renewable energy generation and expanded adoption of electric vehicles present an array of functional demands. Critical to battery function are electron and ion transport as they determine the energy output of the battery under application conditions and what portion of the total energy contained in the battery can be utilized. This review considers electron and ion transport processes for active materials as well as positive and negative composite electrodes. Length and time scales over many orders of magnitude are relevant ranging from atomic arrangements of materials and short times for electron conduction to large format batteries and many years of operation. Characterization over this diversity of scales demands multiple methods to obtain a complete view of the transport processes involved. In addition, we offer a perspective on strategies for enabling rational design of electrodes, the role of continuum modeling, and the fundamental science needed for continued advancement of electrochemical energy storage systems with improved energy density, power, and lifetime.

Active Damping Stabilization Techniques for Cascaded Systems in DC Microgrids: A Comprehensive Review

Microgrids have arisen as an alternate solution to the problem of power generation in distributed energy. Compared to ac microgrids, dc microgrids (DC MGs) are superior in terms of system efficiency, power quality, affordability, and ease of control. For the integration of renewable energy generation into microgrids, power electronic converters are required. When power electronics converters are tightly regulated, they behave as a constant power load (CPL) which exhibits a negative incremental impedance characteristic. As a result, oscillations occur in voltage response at a DC bus. To suppress the voltage oscillations in DC MGs, various damping stabilization techniques are proposed by researchers. This paper provides a comprehensive review on active damping stabilization techniques. To improve the system’s stability, active damping can be implemented in three distinct zones: source-side, load side or CPL side, and intermediate level. After analyzing each zone, their merits and drawbacks have been presented. Moreover, CPL modelling, performance of cascaded DC-DC systems, the effect of the load on the source subsystem, the effect of the source on the load converter, and stability analysis are discussed. Finally, concluding remarks and future research directions are highlighted.

Reactive power control in renewable rich power grids: A literature review

AbstractRecently, there is vast progress in the integration of renewable energy generators (REGs) into the power grid. The reactive power, which is indeed a fundamental power system factor is being influenced highly by the renewable power penetration levels. As a consequence, this has paved the way toward the generation of steady‐state voltage and dynamic/transient stability issues. Therefore, to provide a sustainable and dependable power grid, it is indeed important to maintain and control adequate reactive power reserves. This study introduces a comprehensive literature survey on “management of reactive power in renewable‐rich power networks”. A total of 75 research publications have been collected for this study, and a concise summary of each article is offered. The articles are categorized according to the types of REGs used, type of control, grid code consideration, controller device, implementation tools, grid type, load type as well as various reactive power support systems. In addition, this study examines the advantages and drawbacks of existing models. The performance recorded in each of the collected papers are discussed. Ultimately, the most critical challenges related to management of reactive power in renewable‐rich power grids are identified, along with some essential methodological suggestions for the electricity sector, regulators, and research scientists.

An Advanced Composite Membrane for the All-Vanadium Redox Flow Battery

Redox flow batteries (RFBs) are a promising technology for grid scale stationary energy storage to complement renewable energy systems. These batteries have a relatively low energy density; however, they offer important advantages, including: long life-time; decoupled energy (arbitrarily large electrolyte volume) and power (electrode area); high round-trip efficiency; scalability and design flexibility; fast response; and low environmental impacts. These advantages make them superior to many energy storage technologies for stationary applications [1-4]. Among the various types of RFBs, vanadium RFBs (VRFBs) are an emerging technology for grid scale energy storage and the integration of renewable energy generation [5]. The membrane is a key component of a VRFB that separates the two half-cell electrolytes and prevents cross-mixing, while allowing the transport of ions during charge-discharge cycles [6]. The VRFB membrane should exhibit low vanadium ion permeability to minimize self-discharge, low cost, and long‐term chemical stability under normal operating conditions. A high proton conductivity and low vanadium ion crossover are known to improve the efficiency of VRFBs [6-7]. In this study, we present a novel composite Nafion based membrane that results in a significant increase in the VRFB performance. The composite membrane has been characterized for its chemical, structural, and thermal properties using appropriate analytical techniques. The battery performance was evaluated in a flow cell using a ‘zero-gap’ design with an electrode area of 5 cm2. The electrolytic solution, 1.6 M VOSO4 in 3 M H2SO4, was circulated through the cell. Thermally treated carbon papers were used as the cathode and anode electrodes. For charge-discharge experiments, a constant current density (10 to 80 mA cm−2) was applied with upper and lower voltage cut-offs of 1.65 and 0.8 V, respectively. The stability of the battery using the composite membrane was evaluated over 100 cycles. Figures 1 and 2 show the energy efficiency and capacity retention during 100 charge-discharge cycles. The results reveal that the energy efficiency was improved from 51% to 63% by using the composite membrane. In addition, the charge-discharge capacity and capacity retention improved by around 200% and 25%, respectively. This improvement can be attributed to a higher proton conductivity and lower vanadium permeability of the composite membrane.

Dynamic electricity tariffs: Designing reasonable pricing schemes for private households

As a central mechanism for promoting demand-side management, dynamic electricity tariffs are associated with several advantages. By balancing supply and demand, they not only limit the skyrocketing costs of grid stability, but also support the integration of renewable energy generation in electricity grids and thereby reduce CO2 emissions. Furthermore, dynamic rather than constant electricity unit charges are associated with an increase in overall economic efficiency, due to their capability to reflect time-varying costs of electricity provision. Based on the assumption that households’ adoption of dynamic tariffs is efficiency augmenting, we design and analyze dynamic tariffs that aim to create cost savings sufficient to overcome adoption barriers. Taking into account households’ heterogeneity, we demonstrate that EEX spot market prices are inferior to the forecasted share of renewable energy production and the forecasted residual load as a basis for price signals in terms of typical German households’ monetary savings and CO2 emission reductions. Considering general principles of tariff design, we analyze the average short-term price spread as the key tariff characteristic.

Harmonics and Interharmonics Detection Based on Synchrosqueezing Adaptive S-Transform

The integration of renewable energy generation and nonlinear power electronic equipment into the grid brings about complex harmonics and interharmonics problems. The amplitude and frequency of harmonics and interharmonics should be detected by high time-frequency (T-F) resolution methods owing to their time-varying transient features. In this paper, a synchrosqueezing adaptive S-transform (SAST) method is proposed to detect the parameters of harmonics. Firstly, the time-frequency spectrum (TFS) of the harmonic signals is acquired by an adaptive S-transform (AST) algorithm. The TFS results are then subjected to synchronous compression, so as to achieve higher time-frequency representation precision. The detection results of the simulation signals show that SAST can achieve a better time-frequency resolution than the S-transform (ST) and synchrosqueezing short-time Fourier transform (SSTFT). In addition, the application of SAST to the analysis of experimental signals also suggests its superiority in the parameter detection of harmonics, especially for the time-varying interharmonics.

A CNN-Assisted deep echo state network using multiple Time-Scale dynamic learning reservoirs for generating Short-Term solar energy forecasting

The integration of renewable energy generation presented an important development around the globe and conveys countless financial, commercial, and environmental assistance. However, the random and non-linear nature of solar power generation, the significant perception of solar system may challenge the comprehension and operation of the existing power system. Nowadays, a smart energy forecasting system is important for solar energy generation to provide high-quality energy for end-users to enhance the power system’s reliability and operation. Inspired by the performance and advancements of deep learning approaches in the field of energy, a CNN-assisted deep echo state network (CNN-DeepESN) with principal components analysis (PCA) method is proposed for generating short-term solar energy forecasting. In this study, we designed a hybrid deep learning method to utilized one-hour solar forecasting ahead with five-minute time intervals. The CNN is used to prepare to compose the initial tensor of input solar time-series data and fed to the DeepESN model to compute the features with a high dimensional space by reservoir that avoid the model complexity due to its untrained and sparse nature. Therefore, we adopt the PCA for dimensionality reduction to minimize the computational cost since it offers significant advantages. We conducted extensive experiments to demonstrate and evaluate the proposed system performance by using authentic data collected from the Australian city, Alice Springs. Additionally, conducted a comparative analysis with other state-of-the-art models to illustrate the superiority of the proposed system. Three main evaluation matrices such as MAE, MAPE, and RMSE are used and achieves 0.0381, 3.3313, and 0.3101 score, respectively to show the model robustness and significance in energy forecasting and generation.

Impact of Wind Farm Integration on LMP in Deregulated Energy Markets

The integration of renewable energy generation affects the operating characteristics of a power system, such as electric losses, voltage profile, generation cost, system stability, and reliability of the system. The installation of renewable energy generation units in non-optimal locations may increase system losses, costs, voltage fluctuations, etc. The main hurdle in integrating renewable energy generation units with an existing electrical grid is the uncertainty of renewable sources. This paper presents the impact of wind farm integration on the system economy in a wind-integrated deregulated power market. The importance of deregulation in terms of the system generation cost, bus voltage profile, and locational marginal pricing (LMP) are also studied in this work. LMP is the main parameter responsible for handling the system economy (i.e., profit of generating units and profit of customers). Considering the variable nature of wind flow, three different real-time wind speed datasets are used to validate this work. Bus sensitivity factor (BSF) is considered for equating the optimal position of the wind farm in the integrated system. Five different optimization techniques, i.e., sequential quadratic programming (SQP), artificial bee colony (ABC) algorithms, particle swarm optimization (PSO), ant colony optimization (ACO) algorithm, and slime mold algorithm (SMA), are introduced to solve the optimal power flow problem. The SMA and ACO are used for the first time in this type of economic assessment (i.e., impact valuation of LMP) in a deregulated power system, which is the novelty of this work. The entire work is performed in a modified IEEE 30 bus test system.

Guest Editorial: Enhancing hosting capability for renewable energy generation in active distribution networks

Guest Editorial: Enhancing hosting capability for renewable energy generation in active distribution networks

Research on Distributed Energy Consensus Mechanism Based on Blockchain in Virtual Power Plant.

Virtual power plant (VPP) composed of a large number of distributed energy resources (DERs) has become a regional multienergy aggregation model to realize the large-scale integration of renewable energy generation into the grid. Due to the characteristics of centralized management, the existing energy operation mode is difficult to simply apply to distributed energy resources transactions. The decentralization, transparency, contract execution automation and traceability of blockchain technology provide a new solution to the aggregation of decentralized resources and the opacity of transactions in VPP. In this paper, the existing problems of virtual power plants are analyzed, and the virtual power plant trading model is designed, which realizes the transparent benefit distribution and message transmission of virtual power plants. The virtual power plant blockchain network based on blockchain technology in this model solves the DERs coordination problem in VPP and the security and efficiency problems in information transmission. Combined with the actual situation of virtual power plant, the blockchain network collaboration mechanism (BNCM), which is convenient to reach agreement, is designed. Compared with the traditional practical Byzantine fault tolerance (PBFT) consensus algorithm, this mechanism can make DERs reach a consensus quickly. Finally, simulation experiments on the consensus algorithm show that the algorithm can reduce the collaboration time between DERs under the premise of ensuring the same fault tolerance rate and is more suitable for VPP scenarios with a large number of DERs.

Innovative desalination of geothermal wastewater supported by electricity generated from low-enthalpy geothermal resources

The integration of renewable energy generation with water treatment techniques, based on geothermal water and energy resources, represents a new and interesting research direction. This paper presents a model study aimed at evaluating the twofold utilisation of geothermal water potential: as an energy resource necessary for the desalination process and as a source of wastewater used in the desalination process. The key objective of this work is to critically evaluate the feasibility of integrating electricity generation and technological processes based on membrane techniques solely on the basis of the potential of low-enthalpy geothermal waters. The results of the use of two steps of BWRO on a semi-production scale showed a high quality of permeate in accordance with physical, chemical, microbiological, potential organic and toxic substance contents and also its radiological characteristics. With respect to the feasibility of implementing the organic Rankine cycle or the Kalina cycle, an analysis was carried out that focused on using geothermal water extracted in the temperature range of 80–95 °C. The mass flow rate of geothermal water directed to the power plant was assumed to be in the range 10–150 kg/s. The mineral content of the geothermal water used in the model was 3 g/L.