Fluctuations In Renewable Generation Research Articles

Smart home load scheduling system with solar photovoltaic generation and demand response in the smart grid

This study introduces a smart home load scheduling system that aims to address concerns related to energy conservation and environmental preservation. A comprehensive demand response (DR) model is proposed, which includes an energy consumption scheduler (ECS) designed to optimize the operation of smart appliances. The ECS utilizes various optimization algorithms, including particle swarm optimization (PSO), genetic optimization algorithm (GOA), wind-driven optimization (WDO), and the hybrid genetic wind-driven optimization (HGWDO) algorithm. These algorithms work together to schedule smart home appliance operations effectively under real-time price-based demand response (RTPDR). The efficient integration of renewable energy into smart grids (SGs) is challenging due to its time-varying and intermittent nature. To address this, batteries were used in this study to mitigate the fluctuations in renewable generation. The simulation results validate the effectiveness of our proposed approach in optimally addressing the smart home load scheduling problem with photovoltaic generation and DR. The system achieves the minimization of utility bills, pollutant emissions, and the peak-to-average demand ratio (PADR) compared to existing models. Through this study, we provide a practical and effective solution to enhance the efficiency of smart home energy management, contributing to sustainable practices and reducing environmental impact.

Risk assessment and alleviation of regional integrated energy system considering cross-system failures

Regional integrated system (RIES) bridges the gaps among different energy systems by various energy conversion equipment. While this integration brings forth numerous benefits, it introduces cross-system failures, where a contingency in one system may affect others. This poses significant challenges when evaluating the consequences of such contingencies. To address this, a risk assessment approach is developed for RIES, considering cross-system failures, as well as fluctuations in renewable generation and multi-energy loads. Event tree analysis (ETA) is adopted to simulate the propagation of failures and identify the affected components. Nine risk indices are proposed to quantify the operational risk of RIES at various levels. Additionally, root cause identification and weak point location are developed to formulate risk alleviation strategies. To validate the proposed method, a RIES with 101 nodes is utilized for risk assessment, and multiple risk alleviation strategies are determined. The results demonstrate the effectiveness of the proposed method, providing valuable insights for operators in ensuring the safety of RIES.

Design and CHIL testing of microgrid controller with general rule-based dispatch

Energy Management for microgrids with various compositions and sizes is challenging for conventional optimization-based approach which requires complex problem formulation and high computing power. A microgrid controller with general rule-based dispatch adaptive to different microgrid compositions and operating modes is presented in this article. Several basic dispatch rules are designed with objectives of balancing the total power, tracking grid power import/export reference, mitigating renewable generation fluctuation and reducing energy storage losses. Mathematical formulation and graphical solutions for rules design are described. The basic rules are then combined to form generalized dispatch strategies capable of operating in different scenarios. Four types of dispatch strategies and their variants in terms of dispatchable generator control are introduced. Recommendation of the dispatch types for different microgrid configurations and the adaptivity to microgrid composition variation are also given. The microgrid controller is validated with comprehensive testing and comparative study under various operating points and modes on a controller hardware-in-the-loop (CHIL) testbed.

Siting and sizing of energy storage for renewable generation utilization with multi-stage dispatch under uncertainty: A tri-level model and decomposition approach

For grids suffering from large-scale renewable generation curtailment, the reasonable allocation of energy storage can smooth renewable generation fluctuation for better utilization. This paper analyzes the optimal non-profit planning of energy storage in a grid rich in renewable generation from the perspective of third-party investors. First of all, to model the prediction errors of wind and solar output, a multi-stage scenario tree of intraday uncertainty is established to describe the stochastic output of wind and photovoltaics. In addition, scenario reduction techniques and nonanticipativity constraints are adopted to realize multi-stage optimization. Second, aimed to improve social welfare with an acceptable cost, a tri-level optimization model is established to describe storage siting and sizing, storage dispatch, and market clearing respectively to reach a high level of renewable generation utilization. Furthermore, Karush–Kuhn–Tucker conditions and linearization techniques are used to reform the tri-level model into a linear bi-level one. Third, a decomposition algorithm and a differential cut are proposed to solve the bi-level problem. The optimum is gradually approached via iterating by adding differential cuts and integer cuts. Finally, the model and method are verified based on a region of the HRP-38 system. The results show that storage investment aimed at social welfare can effectively achieve a much higher goal of renewable generation utilization than individual profit-seeking storage investment.

A soft actor-critic deep reinforcement learning method for multi-timescale coordinated operation of microgrids

This paper develops a multi-timescale coordinated operation method for microgrids based on modern deep reinforcement learning. Considering the complementary characteristics of different storage devices, the proposed approach achieves multi-timescale coordination of battery and supercapacitor by introducing a hierarchical two-stage dispatch model. The first stage makes an initial decision irrespective of the uncertainties using the hourly predicted data to minimize the operational cost. For the second stage, it aims to generate corrective actions for the first-stage decisions to compensate for real-time renewable generation fluctuations. The first stage is formulated as a non-convex deterministic optimization problem, while the second stage is modeled as a Markov decision process solved by an entropy-regularized deep reinforcement learning method, i.e., the Soft Actor-Critic. The Soft Actor-Critic method can efficiently address the exploration–exploitation dilemma and suppress variations. This improves the robustness of decisions. Simulation results demonstrate that different types of energy storage devices can be used at two stages to achieve the multi-timescale coordinated operation. This proves the effectiveness of the proposed method.

Probabilistic energy flow calculation for regional integrated energy system considering cross-system failures

With the increasing coupling among multiple energy systems, component failure of one system may spread to other systems, which results in cross-system failures that will remarkably affect the operation state of RIES. In this regard, this paper proposes a probabilistic energy flow calculation approach for regional integrated energy system (RIES), taking into account those cross-system failures (CSF), as well as fluctuations of renewable generation and multi-energy loads. An iterative screening method based on failure identification matrix is developed to find failed components cross different systems caused by CSF. With this method, all CSFs can be divided into three categories to simplify the interaction analysis among systems. Finally, a Monte Carlo based probabilistic energy flow calculation framework is proposed considering those three types of CSF. Numerical studies are performed on a RIES test system with 101 nodes and a practical RIES system with 119 nodes, which verifies the effectiveness of the proposed method. The results also show that the approach can provide more comprehensive insight of the interaction among different energy systems in various failure situations.

Distributionally robust optimal dispatch modelling of renewable-dominated power system and implementation path for carbon peak

The renewable-dominated power system has a high proportion of renewable generation, which promotes the achievement of the carbon peak and carbon neutrality objectives in the power industry. However, renewable generation is dependent on volatile and unpredictable natural resources. Moreover, the renewable resources and load demands are inversely distributed in some countries such as China. Besides, renewable generation is not in sync with load demands. Therefore, the renewable-dominated power system needs a high proportion of cross-region power transmission and requires more reliable dispatching to maintain the generation/load balance than conventional systems. At first, the renewable-dominated power system is divided into three levels in this paper, the cross-regional transmission level, renewable generation level, and thermal power generation level. A moment uncertainty set is employed to indicate the uncertainty caused by fluctuations in renewable generation. Furthermore, the distributionally robust conditional value-at-risk (DRCVaR) is presented to show the risk of abandoning renewable generation under different weather conditions. Based on the three-level construction and other constraints, a distributionally robust optimal dispatch model is proposed to minimize the carbon emission and the abandonment of renewable generation in the system. Then, the proposed dispatch model is converted into a semidefinite programming problem by the dual optimization theory, which can be easily solved. Finally, the proposed dispatch model is verified with a receiving power system in China. Additionally, the implementation path for the carbon peak of the power industry is proposed. The analysis results show that renewable generation reaches 59% in 2025, and the carbon peak reaches its peak value in 2030. In other words, the carbon peak target can be achieved via the renewable-dominated power system with the proposed dispatch model.

Multiobjective Economic Optimal Dispatch for the Island Isolated Microgrid under Uncertainty Based on Interval Optimization

In order to analyse the impact of renewable generation and load uncertainties on the economic operation optimization of the island microgrid, a multiobjective economic optimal dispatch model under uncertainty based on interval optimization is proposed in this paper. The mathematical model of distributed generation and the prediction model of wind speed and wave generation are established. The uncertainties of renewable generation and load are described by the interval mathematical method. On this basis, the interval multiobjective optimal dispatch model is presented. For the “battery disgusting” users on the island, the battery cost is regarded as a separate optimization objective, and a multiobjective optimization objective function to minimize the economic cost, battery cost, and pollution emission of the island microgrid is discussed. An island microgrid, composed of wind turbine, photovoltaic, wave energy generation, diesel generator, and energy storage system, is chosen as a case study. The NSGA-II algorithm is applied to solve the multiobjective optimal problem. The results for deterministic forecast data and load are analysed, and the optimal operation scheme is obtained by the improved multiobjective grey target decision-making method. The influence of renewable generation fluctuations ±10%, ±20%, and ±30% and the load fluctuations ±10% and ±20% on island microgrid operation optimization is discussed in detail, respectively. The relevant research results can provide a reference for formulating the operating scheme of the island microgrid.

A Simulation Environment for Training a Reinforcement Learning Agent Trading a Battery Storage

Battery storages are an essential element of the emerging smart grid. Compared to other distributed intelligent energy resources, batteries have the advantage of being able to rapidly react to events such as renewable generation fluctuations or grid disturbances. There is a lack of research on ways to profitably exploit this ability. Any solution needs to consider rapid electrical phenomena as well as the much slower dynamics of relevant electricity markets. Reinforcement learning is a branch of artificial intelligence that has shown promise in optimizing complex problems involving uncertainty. This article applies reinforcement learning to the problem of trading batteries. The problem involves two timescales, both of which are important for profitability. Firstly, trading the battery capacity must occur on the timescale of the chosen electricity markets. Secondly, the real-time operation of the battery must ensure that no financial penalties are incurred from failing to meet the technical specification. The trading-related decisions must be done under uncertainties, such as unknown future market prices and unpredictable power grid disturbances. In this article, a simulation model of a battery system is proposed as the environment to train a reinforcement learning agent to make such decisions. The system is demonstrated with an application of the battery to Finnish primary frequency reserve markets.

Assessing Blackout Risk With High Penetration of Variable Renewable Energies

We propose a method to analyze the risk of blackouts with high penetration of variable renewable energy sources (VRESs). We consider a model for the long-term evolution of the power grid including propagation of cascading failures, day-to-day fluctuations of renewable generation and moderate use of storage. We analyze grid resilience and stress as VRESs are progressively incorporated. We also evaluate the VRES performance as the average fraction of daily demand covered by renewables.We find that in general, VRES intrinsic variability increases the grid stress and the blackout risk. However, if VRESs are implemented in a distributed way, the spatial spreading of the generation may have a positive effect on grid resilience. As a case study, we analyze the replacement of conventional power plants by solar photovoltaic generation combined with storage in the power grid of the Balearic Islands. We also consider the use of source redundancy and briefly discuss the potential of wind energy.

Estimating the impact of variable renewable energy on base-load cycling in the GB power system

Between 2009 and 2017 the share of wind and solar energy sources in the GB electricity generation mix increased from 2.5% to 17%. Due to the variable nature of these renewable sources, large thermal power stations designed for constant base-load operation have been required to operate more flexibly to compensate for fluctuations in renewable generation. This flexible operation results in increased thermal stress and reduced efficiency causing increased operation, maintenance and fuel costs for these assets. In this paper we present the results of what is, to the best of our knowledge, the first empirical study on the impact of renewables generation on startups, ramping and part-loading (collectively, ‘cycling’) of base-load generators. We develop regression models using half-hourly generation data from 2009 to 2017 that capture the impact of increased renewable penetration while taking into account confounding factors including seasonality and demand. We find that with 2009-levels of renewable generation, cycling in 2017 would have been less severe, with 20% fewer startups. We also present estimates for cycling under National Grid Future Energy Scenarios to 2030 with implications for investment in generation assets. Additionally, the dataset derived in this research is made available and comprises the first open-access dataset on cycling.

Data-Adaptive Robust Transmission Network Planning Incorporating Post-Contingency Demand Response

The significant development and increasing deployment of renewable generation in the modern power system introduces the challenge for dealing with uncertainty. In this paper, data-adaptive robust optimization is applied to the transmission network planning. By taking historical data correlation into account, the proposed model can achieve a lower expansion investment without sacrificing the robustness. Demand response is embedded in this model to relieve the overflow incurred by renewable generation fluctuation and $N-1$ contingency. The model is decomposed into a master problem and several slave problems by column and constraint generation algorithm and then solved iteratively. The numerical simulation tested on Garver 6-bus system and the IEEE 118-bus system demonstrates the effect of demand response in reducing or postponing network construction. The proposed data-adaptive robust optimization is proved to be cost-effective and computationally efficient.

Energy and Frequency Hierarchical Management System Using Information Gap Decision Theory for Islanded Microgrids

In this paper, a robust energy management system is proposed for islanded microgrids, which at the same time considers static modeling of system frequency. The aim of this paper is to manage frequency excursions produced from load and renewable generation fluctuations. In microgrids, the use of inertialess and small-scale energy resources risks the frequency stability. In order to overcome this problem, first, the frequency-dependent behavior of the distributed energy resources is formulated precisely within the centralized hierarchical energy and reserve management context of a microgrid. Then, in order to handle microgrid uncertainties in a robust way, information gap decision theory (IGDT) technique is proposed. Furthermore, to address a robust hierarchical energy and frequency reserve management architecture, the problem is transmitted into a single-level mixed integer linear programming model and solved appropriately over a 24-h scheduling time horizon. Numerical simulation results obtained on a typical islanded smart microgrid are presented, including the demand response mechanism. The IGDT can help microgrid central controller (MGCC) to make operational decisions in front of major uncertainties. The obtained results verify that through the proposed IGDT-based energy management system, the MGCC can effectively stabilize the microgrid frequency along with its economic targets while considering severe uncertainties.

A Framework for Evaluating the Resilience of Dynamic Real-Time Market Mechanisms

Fluctuations in renewable generation motivate a need for real-time coordination of distributed energy resources (DERs) such as demand response resources. Dynamic real-time market mechanisms are a promising approach to facilitate coordination of DERs. However, the implementation of such mechanisms over a large geographic region requires communication across networks which can introduce non-negligible latency, congestion, and packet loss. Such network disruptions raise questions regarding grid resilience, especially, when combined with physical outages. In this paper, we propose a framework for evaluating the resilience of dynamic real-time market mechanisms under a variety of network scenarios that are indicative of real-time implementation challenges. We develop a non-dimensional metric, which takes into account the impact of a physical contingency, and can be used to evaluate the ability of a dynamic market mechanism to adapt to and recover from this contingency. We evaluate the resilience of a recently proposed dynamic real-time market mechanism on the IEEE 118-bus system following a single generator outage in the presence of communication outages.