Uncertain Renewable Energy Research Articles

An optimized decentralized peer-to-peer energy trading system for smart grids incorporating uncertain renewable energy sources through fuzzy optimization

The significant issue in today's world is that the power system is undergoing on account of unafforded power generation and distribution. Consequently, improper utilization of renewable energy, as well as its pricing mechanism of electrical energy, is also a vital issue in transmission and distribution systems (TDS). In addition, in the context of upcoming power systems, ensuring energy stability and effective energy management have become paramount. This paper introduces a novel approach to address these challenges by proposing an optimal demand response grid-connected decentralized peer-to-peer (P2P) energy trading system. This system efficiently manages energy transactions among prosumers, the grid, and renewable energy sources (RES) under uncertain conditions. Fuzzy optimization (FO) is employed to facilitate interconnections between these entities. A fuzzy logic controller with intelligent rules governs the overall strategy and pricing mechanism based on prosumer power deviation (PPD). Inspired by human thinking, these rules prioritize P2P transactions, with surplus energy exported to the main grid (MG) and energy imported from the MG when necessary. Pricing adjusts dynamically with load demand. Simulation results demonstrate the effectiveness of the fuzzy rules across various conditions, ensuring proper system operation. The proposed model also proves that the P2P energy trading system has reduced power consumption by balancing the generated power and the load demand. The proposed system is environmentally friendly, emitting no harmful gases.

Short-term optimal scheduling of wind-photovoltaic-hydropower-thermal-pumped hydro storage coupled system based on a novel multi-objective priority stratification method

In the new power system with high proportion of uncertain renewable energy sources (RES), there is a defect of RES consumption at the expense of other power sources' operational efficiency. This paper proposes a short-term optimal scheduling model of wind-photovoltaic-hydropower-thermal-pumped hydro storage (WPHTPHS) coupled system, which realizes the multiple optimization objectives involving minimizing operation cost of thermal power units, maximizing clean energy power generation, minimizing net load fluctuation and thermal power regulation. First, to overcome the dimension disaster problem in the solution space of high-dimensional random variables, a method for pre-solving integer state variables is proposed. Then, a novel multi-objective solution strategy of priority stratification-coupled feedback combined with improved plant growth simulation algorithm is designed. Finally, the effectiveness and superiority of the proposed model and solution method are demonstrated by case studies, and the numerical results show that the number of startups and shutdowns, standard deviation of output and operating cost of thermal power units are reduced by 90.9 %, 65.34 %, and 14.01 % respectively, compared with traditional wind-photovoltaic-thermal strategy. This study contributes to resolving the relationship between conflicting objectives and highlighting the potential advantages of WPHTPHS coupled system to maximize overall performance from economic and stability perspectives.

Data-driven two-stage scheduling of multi-energy systems for operational flexibility enhancement

The multi-energy system, encompassing electricity networks, district heating networks (DHNs), and hydrogen-enriched compressed natural gas (HCNG) networks, provides an alternative for enhancing operational flexibility. This paper investigates a data-driven two-stage scheduling strategy of multi-energy system to promote uncertain renewable energy integration and improve economic benefits. Dynamic models for DHNs and HCNG networks are established, and the flexibility of multi-energy system is quantified through distribution-level power aggregation. To compromise flexibility enhancement and operational cost reduction, the multiobjective day-ahead scheduling optimization is conducted based on adaptive batch-ParEGO method. Taking day-ahead scheduling as a baseline, a data-driven real-time dispatch method is proposed based on the deep deterministic policy gradient algorithm, which adaptively modifies the energy management scheme in smaller temporal dimension to address uncertainties on both the load and source sides. The performance of the proposed two-stage scheduling method is demonstrated through numerical tests.

An individualized adaptive distributed approach for fast energy-carbon coordination in transactive multi-community integrated energy systems considering power transformer loading capacity

The optimization of multi-community integrated energy systems (MCIES), as a proficient means of integrating distributed energy resources and diverse loads, poses a compelling challenge. This challenge revolves around strategically harnessing the energy-carbon synergy advantages within these systems, while factoring in the heterogeneity among different communities and the constraints imposed by electrical connectivity devices. Facing the challenge, this paper proposes an individualized adaptive distributed approach for energy-carbon coordination in MCIES with power transformers (PT-MCIES). Firstly, a feasible region is constructed based on current and temperature limits to assess the loading capacity of power transformers. Based on this, a transactive energy (TE) based co-optimization model for PT-MCIES considering energy sharing and carbon trading is developed, where rolling horizon optimization is adopted to cope with the randomness from renewable energy and loads. For the TE problem, the alternating direction method of multipliers (ADMM) algorithm is employed to achieve distributed optimization. Uncertain renewable energy and loads, along with variable rolling horizons, make it difficult for the traditional ADMM to ensure convergence speed in the co-optimization of PT-MCIES. Accordingly, an individualized adaptive ADMM (IAADMM) embedded a spectral penalty parameter selection rule is adopted. Simulation results show that the proposed scheduling model can effectively guarantee the safe operation of power transformers with the suggestion of a feasible region, and achieve higher economic and environmental benefits than the reference one that ignores energy-carbon coordination. Furthermore, the proposed IAADMM exhibits superior performance in terms of convergence speed and robustness to the initial penalty parameter when compared to ADMM and its various variants.

Risk-aware participation in day-ahead and real-time balancing markets for energy storage systems

The increasing volatility of electricity prices due to increased uncertain renewable energy generation gives rise to interesting short-term arbitrage opportunities for Energy Storage Systems (ESS) operators. Whereas prior research has shown the possibility to exploit inter-temporal arbitrage opportunities in the real-time balancing market, this paper formulates a two-stage optimization methodology that allows ESS operators to also engage in inter-market arbitrage by participating in both the day-ahead and real-time balancing markets. However, the effectiveness of such a strategy is heavily influenced by expected inter-market price differentials, which is known to be difficult to predict when it involves the imbalance price. To address this issue, we propose a risk-averse approach by incorporating the conditional value at risk in the ESS objective function. The proposed methodology is applied to a case study of the Belgian electricity market, where we demonstrate the effectiveness of (i) the combined market participation compared to an ESS participating in either market, and (ii) the risk-aware methodology by showcasing improved ex-post out-of-sample profit performance of a risk-averse compared to a risk-neutral ESS.

Multi-timescale optimization of integrated energy system with diversified utilization of hydrogen energy under the coupling of green certificate and carbon trading

Promoting renewable energy and developing low-carbon integrated energy systems are noteworthy in the energy sector. However, in existing works on the integrated energy system, the coupling of green certificate and carbon trading mechanism under diversified utilization of hydrogen energy has not been fully considered to provide an incentive effect for uncertain renewable energy resources. Based on these considerations, a multi-timescale optimization of an integrated energy system with diversified utilization of hydrogen energy is studied under the coupling of green certificate and carbon trading mechanism. First, the coupled interaction mechanism of green certificate trading-stepped carbon trading is proposed by linking stepped carbon trading to green certificate trading through green certificates. Furthermore, the establishment of the diversified utilization unit of hydrogen energy addresses carbon emissions from the perspective of energy application and offers fresh concepts for a low-carbon economy. Then, an optimized scheduling model for multiple time scales of day-ahead/intraday/real-time is developed to reduce the impact of renewable energy prediction errors on system operation. Results acquired from case studies demonstrate the effectiveness in terms of achieving economic and low carbon. The accommodation level of renewable energy with a coupling mechanism improves by 3.08% compared to that without considering coupling. In addition, the diversified utilization of the hydrogen energy model enables the reduction in total cost and carbon emission by 5.14% and 2.4927 tonnes in comparison to the traditional power-to-gas mode.

Adjustable Robust Energy Operation Planning under Uncertain Renewable Energy Production

Adjustable Robust Energy Operation Planning under Uncertain Renewable Energy Production

Multi-scenario flexibility assessment of power systems considering renewable energy output uncertainty

The widespread adoption of renewable energy sources presents a significant challenge to the flexibility of power system. To assess the flexibility of the power system in scenarios with uncertain renewable energy output, it is crucial to quantify it quantitatively. This evaluation plays a vital role in planning flexible regulatory resources and dispatching resources for both the energy source and load. This study introduces a novel flexibility assessment model tailored for power grids with high renewable energy penetration, specifically addressing uncertainty associated with wind and PV. By analyzing the impact of wind and PV uncertainty on system flexibility, the paper proposes an improved cohesive hierarchical cluster analysis method, incorporating reliability considerations based on the Davies-Bouldin classification reliability index. Additionally, the study develops models for flexibility resources and demands within high renewable energy power systems, along with quantitative assessment indicators across three key aspects. Through a structured flexibility assessment process accounting for wind and PV uncertainty, the effectiveness of the proposed approach is validated using real-world data from a renewable energy power grid in Shandong province. A set of typical renewable energy output scenarios with uncertainty is constructed using the improved hierarchical cluster analysis method. The study then analyses the impact of different wind and PV penetration rates and the proportion of energy storage units on system flexibility by the flexibility assessment model to validate the proposed method's effectiveness.

Fast Frequency Response for Future Low-Carbon Indian Power System: Challenges and Solutions

India has transitioned from being an energy-deficient nation to an energy-surplus nation. As of July 2023, India’s power system has seen a rapid transformation, with renewable energy, including large hydro, accounting for 40% of the total installed capacity at 177 GW. The ongoing energy transition has not only changed the country’s energy mix but also brought new challenges for system operators. The primary challenge is frequency instability due to the reduction of system inertia and governor-based primary frequency response (PFR) availability by large-scale integration of uncertain renewable energy resources (RES) such as wind and solar photovoltaic generators. Furthermore, the absence of frequency control ancillary services market, inadequate penetration of electric vehicles (EVs) and uncertainty of RES generation enhance the complexity of the frequency response services in the Indian power system. In this context, this paper presents a comprehensive review of the frequency response challenges of the future low-carbon Indian power system and underlines the new potential fast frequency response (FFR) services from advanced energy storage and renewable generation technologies. Such FFR services can be swiftly deployed in low-inertia grids through the integration of energy storage systems and inverter-based resources in a shorter span. Modeling of FFR services in security constraint unit commitment (SCUC) can enhance grid resiliency and facilitate high penetration of RES in the Indian power system without loss of frequency stability.

A novel multi-objective tuning formula for load frequency controllers in an isolated low-inertia microgrid incorporating PV/wind/FC/BESS

Load frequency control (LFC) is vital for isolated microgrids (IMGs), especially when uncertain renewable energy sources (RESs) are present. Enhancing LFC schemes relies mainly on three tracks. Adding new resources to IMG structures is the first track, designing novel controller structures for LFC schemes is the second, and the third is improving controller tuning procedures in the LFC schemes. This research suggests an innovative multi-objective formula (MOF) for controller tuning that combines a novel error criterion termed the integral-square time absolute error of frequency change with the integral-square of IMG controllers' signals. Tested IMG includes multi-sources of diesel engine generators, fuel cells, battery energy storage technologies, and RESs (like photovoltaic and wind turbines). The proposed MOF tuning is evaluated compared to four different objective functions, which are the integral-absolute error (IAE), integral-time absolute error (ITAE), integral-square error (ISE), and integral-time square error (ITSE). The proportional-integral-derivative (PID), fractional-order, and cascade PID controllers are implemented to appraise the proposed MOF extensively against all these single objectives. Statistical analyses are accomplished comprehensively to verify the effectiveness of the artificial rabbits' optimization algorithm (ARO) in competition with other recent optimization algorithms to tune different controllers utilized in the LFC schemes of the examined IMG based on tested objectives. Therefore, ARO is applied to optimize controller settings by combining system nonlinearities with IMG sources' maximal generation rate constraints. The comparative analysis considers settling times, overshoots, IAE, ITAE, ISE, and ITSE performance indices. MATLAB/Simulink simulations confirmed the ability of the suggested MOF tuning to stabilize the system and keep improving performance indices, significantly attaining the minimum settling time even for massive three-type load fluctuations. The first type is step disturbances ranging between 0.1 and 0.25 p.u, the second is varying step disturbances every 5 s, and the third is severe dynamic random load shifts from −0.2 to 0.2 p.u. In addition, the MOF outperforms other competitors' tuning formulas while adding fluctuations of RESs with load disturbances. Furthermore, the robustness analysis is conducted for the applied controllers based on the proposed MOF tuning approach by changing the IMG nominal parameters with ±25 % and adding system nonlinearities. The analysis ensured its efficacy in preserving system stability. Finally, the stability test in the frequency domain using the MATLAB/control design tool verified system stability when different LFC controllers were tuned based on the proposed MOF tuning.

Enhancing network-constrained P2P energy sharing through virtual communities

Peer-to-peer (P2P) exchanges provide opportunities for prosumers to meet their energy needs cost-effectively with the effective utilization of renewable energy sources (RES). Along with the benefits, P2P energy trading also raises issues in distribution network operations. The proposed framework has the potential to handle transactions in regard to network constraints by leveraging the benefits of dynamic utilization-based charges for network usages and power pricing structures. This work proposes a novel P2P energy trading framework comprising virtual communities (VCs) made by grouping buildings located on a particular bus. A cooperative game is formulated for inter- and intra- bus energy trading. A battery energy storage system (BESS) is present in each VC, and a stochastic model is used for coping with the uncertainties related to RESs. In this work, each player’s individual problem is decoupled to achieve equilibrium in a decentralized manner to avoid security and privacy issues. This research focuses on the significant reduction in the net energy cost of the buildings in the presence of uncertain renewable energy generations by taking advantage of the load variability of buildings present on the same and different buses without violating any network constraints and raising any privacy concerns. The proposed framework reduces the building cost by 21.87%.

A Failure Tree Model for Cascading Failure in Power Grid With Uncertain Renewable Energy Generation

A Failure Tree Model for Cascading Failure in Power Grid With Uncertain Renewable Energy Generation

Simultaneous Assessment of Multiple Aspects of Stability of Power Systems With Renewable Generation

Modern power systems are operating much closer to stability limits due to the integration of uncertain Renewable Energy Sources (RES)-based generations and the decommissioning of traditional synchronous generations. Hence, different aspects of power system stability assessment have come into research focus again. Conventional system stability assessment typically uses dedicated stability indices describing a single aspect of system stability (e.g., voltage, frequency, angularity) or composite indices developed for one aspect of system stability. Such assessments can be ambiguous as the impacts of new devices and technologies on different aspects of system stability can be both, positive and negative to different extents. This paper introduces a probabilistic framework and two composite stability indices for the simultaneous assessment of multiple aspects of power system stability. The application of the indices is demonstrated on a range of case studies with modified IEEE 9-bus and modified IEEE 68-bus test systems without/with RES in a mixed Matlab and DigSilent PowerFactory simulation environment.

Quantifying resilient urban energy systems: Statistical analysis of climate adaptation, renewable integration, and socioeconomic dynamics

Multi-energy systems (MES) become increasingly important to accommodate renewable energy (RE) since fossil fuels diminish and RE is more prevalent. Power systems are challenged by high penetrations of RE, including frequent wind curtailments and uncertain RE resources threatening their security. A 2-step scheme for MES is proposed in the study to generate reliable operational decisions by incorporating integrated heat-electricity demand response (DR). In the initial phase, dynamic programming was applied to determine the optimal solution for previous linkage sessions. Subsequently, machine learning techniques were trained using historical data and the resulting optimal decisions to make optimal real-time decisions without prior knowledge of future power costs or vehicle usage. An accurately trained deep neural network is capable of significantly reducing charging costs, usually approaching the retrospectively calculated optimum charging price. In the suggested scheme, the operating prices are minimized under a base scenario ensuring that no constraint is violated regardless of the scenario. RE curtailment can be reduced significantly with power-to-gas (P2G) devices that convert excess energy from wind and photovoltaic sources into natural gas. In addition, this paper proposes integrated heat-electric DR as a means of reducing operating prices and enhancing security against uncertainties by employing couplings between different systems. The outcomes show that robust optimization enhances the security of systems, and the unified electric-heat DR can boost the use of RE while improving the economic benefit. It should be noted that this paper investigated under the impact of the climate changes and also socioeconomic dynamics that can change consuming energy in urban systems.

Robust Optimal Scheduling of Microgrid with Electric Vehicles Based on Stackelberg Game

With increasing penetration of distributed generators (DG), the uncertainty and intermittence of renewable energy has brought new challenges to the economic dispatch and promotion of environment sustainability of microgrids. Active loads, especially in electric vehicles (EVs), are thought to be an efficient way to deal with the uncertainty and intermittence of renewable energy. One of the most important features of EVs is that their demand will vary in response to the electricity price. How to determine the real-time charging price to guide the orderly charging of EVs and operate with an uncertain renewable energy output represents an important topic for the microgrid operator (MGO). To this end, this paper formulates the optimal pricing and robust dispatch problem of the MGO as a Stackelberg game, in which the upper level minimizes the MGO’s cost, while the lower level minimizes the charging cost of each EV. In the problem, the approximate linear relationship between the node voltage and equivalent load is modeled, and the approximate linear expression of the node voltage security constraint is derived. Using dual optimization theory, the robust optimal dispatch model is transformed into a linear programming model without uncertain variables. Then, the Stackelberg game model is transformed into a mixed integer linear program by using the duality theorem of linear programming. Finally, the effectiveness of the proposed method is proved by simulation within the modified IEEE33-bus system.

Data-Driven Sizing of Co-Located Storage for Uncertain Renewable Energy

Data-Driven Sizing of Co-Located Storage for Uncertain Renewable Energy

How do uncertain renewable energy induced risks evolve in a two-stage deregulated wholesale power market

Integrating renewable power into power market transactions is significant in terms of achieving carbon peaking and carbon neutrality. However, the integration of intermittent renewable energy sources (RES) introduces instability into power generation and brings certain risks to deregulated power market transactions. This study aims to identify the risks arising from RES power integration and analyze how these risks transmit and evolve in deregulated power markets. A Cournot game model is constructed to analyze the generation decisions of a thermal power generation company (GENCO) and a wind power GENCO in a two-stage power market. According to the results, in the mid-to-long-term power market, an increase in the uncertainty of wind power generation (WPG) could reduce wind power supply, increase the clearing price and decrease the power demand. Then, in the spot power market, the increased uncertainty related to WPG might decrease the amount of wind power traded, increase the marginal generation cost of the thermal power GENCO, raise the clearing price, and reduce the market demand for power. Finally, the uncertainty related to wind power generation could result in diminished revenue for the wind power GENCO and a loss in overall social welfare in the two-stage power market transaction.

Robust Data-Driven Sparse Estimation of Distribution Factors Considering PMU Data Quality and Renewable Energy Uncertainty - Part I: Theory

Data-driven sparse estimation of distribution factors (DFs) facilitates online power flow sensitivity analysis for secure system operation. However, existing methods are vulnerable to time-varying non-Gaussian PMU measurement noise, bad data, and uncertain renewable energy sources (RESs). Moreover, they lack scalability to large-scale systems. This two-part paper proposes a robust and scalable sparse DF estimation framework considering PMU data quality and RES uncertainty. In this Part I, a novel Adaptive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> -Lasso estimator with theoretically guaranteed robustness is proposed. It mitigates the impacts of measurement and RES uncertainties to yield accurate dominant DF estimates while promoting sparsity. The key idea is to integrate the robust statistics theory with sparse representation techniques, in particular the Huber loss function, adaptively-weighted <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> regularization, concomitant scale estimate, and pseudo-residuals. Two important robustness properties of this estimator are theoretically proven, i.e., the bounded influence function and the asymptotic consistency of dominant DF estimates given limited samples. The breakdown points of this estimator to measurement and RES uncertainties are derived. Test results validate that the proposed estimator allows accurate estimation without relying on power flow models or massive operating data. It achieves significantly superior robustness over existing methods in multiple scenarios.

Linearized AC power flow model based interval total transfer capability evaluation with uncertain renewable energy integration

Total transfer capability (TTC) evaluation plays an essential role in guaranteeing reliable power transactions in a deregulated electricity market. However, the large-scale integration of wind power brings significant challenges to TTC calculations. The conventional probabilistic TTC evaluation methods are highly dependent on the pre-defined probabilistic distributions of the input random variables, which could not be accurately predicted in practical situations. This paper proposes an interval TTC evaluation method based on the linearized AC power flow (LACPF) model, which can provide intuitive TTC boundary information without any prior distributions. The adoption of LACPF also contributes to yielding more accurate TTC values than those DC power flow (DCPF) based methods. The proposed interval optimization model is equivalently translated into an optimistic model and a pessimistic model. The optimistic model is formulated as a max-max problem and can be directly converted to a single-level linear programming model. The pessimistic model is formulated as a min-max problem that cannot be solved directly. Hence, the strong duality theory and the big-M method are introduced to transfer the original bilevel model into a single-level mixed integer linear programming (MILP) model for an efficient solution. The proposed method is validated on the IEEE 118-bus system and a modified actual power grid in Northwest China. Numerical results demonstrate its computational efficiency.

A multi-time scale coordinated control and scheduling strategy of EVs considering guidance impacts in multi-areas with uncertain RESs

With the increased electrification of transportation sector, the electric vehicles (EVs) are deemed to be key players in energy scheduling act to realize more economical operation of distribution networks. EVs have the function of energy space–time transfer, and energy-space coupling effects need to be considered in scheduling. In this paper, EVs’ spatial characteristics from multi-areas and characteristics of transferable charging power are both taken into account, then a multi-time scale coordinated control and scheduling strategy is proposed to achieve optimal schedules in both day-ahead (DA) and real-time (RT) periods. First, in DA periods, EVs are modeled as shiftable and location-flexible loads to participate in multi-areas’ scheduling task managed by a unified distribution system operator (DSO). To attract more spatially distributed EVs to different charging stations and avoid charging congestion, a price-based transfer model (PBTM) is established to realize EVs charging guidance in different areas while integrated into the DA stochastic scheduling. Next, in RT periods, EVs are modeled as controllable loads to compensate RT power errors caused by uncertain renewable energy sources (RESs) and inaccuracies associated with DA prediction. Both DA and RT scheduling are coordinated with a RT control strategy for EVs, in which a state space model (SSM) is constructed to calculate charging power and then form 1-min control signals to realize the tracking of multi-time scale schedule. Simulation results demonstrate that the proposed coordinated control and scheduling strategy can guide more EVs to be grid-connected, promote multi-time scale economic scheduling, and benefit the EV users meanwhile.