Situation Awareness for Smart Distribution Systems

In recent years, the global climate has become variable due to intensification of the greenhouse effect, and natural disasters are frequently occurring, which poses challenges to the situation awareness of intelligent distribution networks. Aside from the continuous grid connection of distributed generation, energy storage and new energy generation not only reduces the power supply pressure of distribution network to a certain extent but also brings new consumption pressure and load impact. Situation awareness is a technology based on the overall dynamic insight of environment and covering perception, understanding, and prediction. Such means have been widely used in security, intelligence, justice, intelligent transportation, and other fields and gradually become the research direction of digitization and informatization in the future. We hope this Special Issue represents a useful contribution. We present 10 interesting papers that cover a wide range of topics all focused on problems and solutions related to situation awareness for smart distribution systems. We sincerely hope the papers included in this Special Issue will inspire more researchers to further develop situation awareness for smart distribution systems. We strongly believe that there is a need for more work to be carried out, and we hope this issue provides a useful open-access platform for the dissemination of new ideas

Renewable energy allocation based on maximum flow modelling within a microgrid

This paper designs an energy allocation scheme based on maximum flow modeling for a microgrid containing renewable energy generators and consumer facilities. Basically, the flow graph consists of a set of nodes representing consumers or generators as well as a set of weighted links representing the amount of energy generation, consumer-side demand, and transmission cable capacity. The main idea lies in that a special node is added to account for the interaction with the main grid and that two-pass allocation is executed. In the first pass, the maximum flow solver decides the amount of the insufficiency and thus how much to purchase from the main grid. The second pass runs the flow solver again to fill the energy lack and calculates the surplus of renewable energy generation. The performance measurement result obtained from a prototype implementation shows that the generated energy is stably distributed over multiple consumers until the energy generation reaches the maximum link capacity

Applications of Probabilistic Forecasting in Smart Grids : A Review

This paper reviews the recent studies and works dealing with probabilistic forecasting models and their applications in smart grids. According to these studies, this paper tries to introduce a roadmap towards decision-making under uncertainty in a smart grid environment. In this way, it firstly discusses the common methods employed to predict the distribution of variables. Then, it reviews how the recent literature used these forecasting methods and for which uncertain parameters they wanted to obtain distributions. Unlike the existing reviews, this paper assesses several uncertain parameters for which probabilistic forecasting models have been developed. In the next stage, this paper provides an overview related to scenario generation of uncertain parameters using their distributions and how these scenarios are adopted for optimal decision-making. In this regard, this paper discusses three types of optimization problems aiming to capture uncertainties and reviews the related papers. Finally, we propose some future applications of probabilistic forecasting based on the flexibility challenges of power systems in the near future.© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Techno-economic evaluation of battery storage systems in industry

In the context of a changing energy system towards one dominated by renewable energy sources, the demand for flexible energy generation and consumption will increase. Battery storage systems can provide a significant share of this energy flexibility, especially when combined with an industrial manufacturing plant to shift the industrial electricity demand over time. This paper contributes to a better understanding of the business decision when investing in a battery storage system and when marketing energy flexibility. For this purpose, the work considers the techno-economic and regulatory framework for flexibility measures and examines the optimal investment and dispatch planning for a battery storage system in an industrial company. The studies in this thesis focus on three central aspects. As a first aspect, the various revenue streams for the stored electricity are analysed and how these influence the profitability of a battery storage system. In particular, the provision of frequency containment reserve power, peak load shifting or peak shaving, arbitrage trading on the energy markets and the increase in self-consumption through photovoltaic self-generation are addressed. For this purpose, an optimisation model is formulated as a discrete, linear programme that maps the economic framework of the flexibility markets and integrates the technological constraints of the battery storage system. As a second aspect, uncertainties about market prices, load and generation behaviour are integrated into the optimisation model and the influence on the investment decision is investigated. This is done on the one hand by a two-stage robust optimisation model, which represents the uncertainty about the market success on the intraday market. On the other hand, the significance of the sequence of uncertain market decisions is illuminated through a multi-stage stochastic optimisation model. As a third aspect, the trade-off between the economic and ecological use of a battery storage system is analysed. For this purpose, an ecological, CO₂-minimal dispatch is calculated by deriving national CO₂-emission factors and compared with an economically optimal dispatch. The case studies are analysed based on real industrial load data from small, medium and large enterprises. The thesis discusses the technical and economic framework conditions, with the main focus on Germany. However, a comparison between the countries Germany, Denmark, and Croatia is also presented. The results show that peak shaving and the provision of frequency containment reserve are complementary and make the investment in a battery storage system economically viable. Self-generation through a photovoltaic system can reduce the risk arising from uncertain energy market prices. However, the sequence of uncertain decisions has a significant impact on the design of the battery storage system. Economically feasible operation through arbitrage trading, on the other hand, is not possible due to the small price differences in the markets and limitations due to battery ageing and efficiency. These battery characteristics also influence the use of a battery storage system for CO₂-reduction. Due to the limited number of cycles and relatively high charging losses, battery technology is currently unsuitable for CO₂-minimal storage use. Nevertheless, the economic and ecological potential of battery storage systems strongly depends on individual factors such as local grid charges, the selected battery technology and the individual industrial load profile. Advances in battery technology, such as increased lifetime, and possible new flexibility markets, such as dynamic grid charges, offer new application and marketing opportunities that could increase the economic viability of a battery storage system

WIND POWER PROBABILISTIC PREDICTION AND UNCERTAINTY MODELING FOR OPERATION OF LARGE-SCALE POWER SYSTEMS

Over the last decade, large scale renewable energy generation has been integrated into power systems. Wind power generation is known as a widely-used and interesting kind of renewable energy generation around the world. However, the high uncertainty of wind power generation leads to some unavoidable error in wind power prediction process; consequently, it makes the optimal operation and control of power systems very challenging. Since wind power prediction error cannot be entirely removed, providing accurate models for wind power uncertainty can assist power system operators in mitigating its negative effects on decision making conditions. There are efficient ways to show the wind power uncertainty, (i) accurate wind power prediction error probability distribution modeling in the form of probability density functions and (ii) construction of reliable and sharp prediction intervals. Construction of accurate probability density functions and high-quality prediction intervals are difficult because wind power time series is non-stationary. In addition, incorporation of probability density functions and prediction intervals in power systems’ decision-making problems are challenging. In this thesis, the goal is to propose comprehensive frameworks for wind power uncertainty modeling in the form of both probability density functions and prediction intervals and incorporation of each model in power systems’ decision-making problems such as look-ahead economic dispatch. To accurately quantify the uncertainty of wind power generation, different approaches are studied, and a comprehensive framework is then proposed to construct the probability density functions using a mixture of beta kernels. The framework outperforms benchmarks because it can validly capture the actual features of wind power probability density function such as main mass, boundaries, high skewness, and fat tails from the wind power sample moments. Also, using the proposed framework, a generic convex model is proposed for chance-constrained look-ahead economic dispatch problems. It allows power system operators to use piecewise linearization techniques to convert the problem to a mixed-integer linear programming problem. Numerical simulations using IEEE 118-bus test system show that compared with widely used sequential linear programming approaches, the proposed mixed-integer linear programming model leads to less system’s total cost. A framework based on the concept of bandwidth selection for a new and flexible kernel density estimator is proposed for construction of prediction intervals. Unlike previous related works, the proposed framework uses neither a cost function-based optimization problem nor point prediction results; rather, a diffusion-based kernel density estimator is utilized to achieve high-quality prediction intervals for non-stationary wind power time series. The proposed prediction interval construction framework is also founded based on a parallel computing procedure to promote the computational efficiency for practical applications in power systems. Simulation results demonstrate the high performance of the proposed framework compared to well-known conventional benchmarks such as bootstrap extreme learning machine, lower upper bound estimation, quantile regression, auto-regressive integrated moving average, and linear programming-based quantile regression. Finally, a new adjustable robust optimization approach is used to incorporate the constructed prediction intervals with the proposed fuzzy and adaptive diffusion estimator-based prediction interval construction framework. However, to accurately model the correlation and dependence structure of wind farms, especially in high dimensional cases, C-Vine copula models are used for prediction interval construction. The simulation results show that uncertainty modeling using C-Vine copula can lead the system operators to get more realistic sense about the level of overall uncertainty in the system, and consequently more conservative results for energy and reserve scheduling are obtained

A series multi-step approach for operation Co-optimization of integrated power and natural gas systems

Power to gas units and gas turbines have provided considerable opportunities for bidirectional interdependency between electric power and natural gas infrastructures. This paper proposes a series of multi-step strategy with surrogate Lagrange relaxation for operation co-optimization of an integrated power and natural gas system. At first, the value of coordination capacity is considered as a contract to avoid dysfunction in each system. Then, the uncertainties and risks analysis associated with wind speed, solar radiation, and load fluctuation are implemented by generating stochastic scenarios. Finally, before employing surrogate Lagrange relaxation, the non-linear and non-convex gas flow constraint is linearized by two-dimension piecewise linearization. In the proposed procedure, constraints for energy storages and renewable energy sources are included. Two case studies are employed to verify the effectiveness of the proposed method. The surrogate Lagrange relaxation approach with coordination branch &amp; cut method enhances the accuracy of convergence and can effectively reduce the decision-making time.</p

Deep Reinforcement Learning for Distribution Network Operation and Electricity Market

The conventional distribution network and electricity market operation have become challenging under complicated network operating conditions, due to emerging distributed electricity generations, coupled energy networks, and new market behaviours. These challenges include increasing dynamics and stochastics, and vast problem dimensions such as control points, measurements, and multiple objectives, etc. Previously the optimization models were often formulated as conventional programming problems and then solved mathematically, which could now become highly time-consuming or sometimes infeasible. On the other hand, with the recent advancement of artificial intelligence technologies, deep reinforcement learning (DRL) algorithms have demonstrated their excellent performances in various control and optimization fields. This indicates a potential alternative to address these challenges. In this thesis, DRL-based solutions for distribution network operation and electricity market have been investigated and proposed. Firstly, a DRL-based methodology is proposed for Volt/Var Control (VVC) optimization in a large distribution network, to effectively control bus voltages and reduce network power losses. Further, this thesis proposes a multi-agent (MA)DRL-based methodology under a complex regional coordinated VVC framework, and it can address spatial and temporal uncertainties. The DRL algorithm is also improved to adapt to the applications. Then, an integrated energy and heating systems (IEHS) optimization problem is solved by a MADRL-based methodology, where conventionally this could only be solved by simplifications or iterations. Beyond the applications in distribution network operation, a new electricity market service pricing method based on a DRL algorithm is also proposed. This DRL-based method has demonstrated good performance in this virtual storage rental service pricing problem, whereas this bi-level problem could hardly be solved directly due to a non-convex and non-continuous lower-level problem. These proposed methods have demonstrated advantageous performances under comprehensive case studies, and numerical simulation results have validated the effectiveness and high efficiency under different sophisticated operation conditions, solution robustness against temporal and spatial uncertainties, and optimality under large problem dimensions