Showing papers on "Electric power system published in 2018"
11 Jun 2018
TL;DR: The challenges of such low-inertia power systems are reviewed, the solutions that have been put forward thus far are surveyed, and the topics of power system stability, modeling, and control are touched upon.
Abstract: The electric power system is currently undergoing a period of unprecedented changes. Environmental and sustainability concerns lead to replacement of a significant share of conventional fossil fuel-based power plants with renewable energy resources. This transition involves the major challenge of substituting synchronous machines and their well-known dynamics and controllers with power electronics-interfaced generation whose regulation and interaction with the rest of the system is yet to be fully understood. In this article, we review the challenges of such low-inertia power systems, and survey the solutions that have been put forward thus far. We strive to concisely summarize the laid-out scientific foundations as well as the practical experiences of industrial and academic demonstration projects. We touch upon the topics of power system stability, modeling, and control, and we particularly focus on the role of frequency, inertia, as well as control of power converters and from the demand-side.
621 citations
TL;DR: In this article, an extensive review on recent advancements in the field of solar photovoltaic power forecasting is presented, which aims to analyze and compare various methods of solar PV power forecasting in terms of characteristics and performance.
539 citations
TL;DR: Pandapower is a Python-based BSD-licensed power system analysis tool aimed at automation of static and quasi-static analysis and optimization of balanced power systems and allows comfortable extension with third-party libraries.
Abstract: Pandapower is a Python-based BSD-licensed power system analysis tool aimed at automation of static and quasi-static analysis and optimization of balanced power systems It provides power flow, optimal power flow, state estimation, topological graph searches, and short-circuit calculations according to IEC 60909 pandapower includes a Newton-Raphson power flow solver formerly based on pypower, which has been accelerated with just-in-time compilation Additional enhancements to the solver include the capability to model constant current loads, grids with multiple reference nodes, and a connectivity check The pandapower network model is based on electric elements, such as lines, two- and three-winding transformers, or ideal switches All elements can be defined with nameplate parameters and are internally processed with equivalent circuit models, which have been validated against industry standard software tools The tabular data structure used to define networks is based on the Python library pandas, which allows comfortable handling of input and output parameters The implementation in Python makes pandapower easy to use and allows comfortable extension with third-party libraries pandapower has been successfully applied in several grid studies as well as for educational purposes A comprehensive publicly available case study demonstrates a possible application of pandapower in an automated time-series calculation
502 citations
TL;DR: An updated overview of currently available modelling tools, their capabilities and to serve as an aid for modellers in their process of identifying and choosing an appropriate model for analysing energy and electricity systems is presented.
Abstract: This paper presents a thorough review of 75 modelling tools currently used for analysing energy and electricity systems. Increased activity within model development in recent years has led to several new models and modelling capabilities, partly motivated by the need to better represent the integration of variable renewables. The purpose of this paper is to give an updated overview of currently available modelling tools, their capabilities and to serve as an aid for modellers in their process of identifying and choosing an appropriate model. A broad spectrum of modelling tools, ranging from small-scale power system analysis tools to global long-term energy models, has been assessed. Key information regarding the general logic, spatiotemporal resolution as well as the technological and economic features of the models is presented in three comprehensive tables. This information has been validated and updated by model developers or affiliated contact persons, and is state-of-the-art as of the submission date. With the available suite of modelling tools, most challenges of today's electricity system can be assessed. For a future with an increasing share of variable renewables and increasing electrification of the energy system, there are some challenges such as how to represent short-term variability in long-term studies, incorporate the effect of climate change and ensure openness and transparency in modelling studies.
454 citations
07 May 2018
TL;DR: The constant growth of air traffic, the demand for performance optimization, and the need for decreasing both operating and maintenance costs have encouraged the aircraft industry to move toward more electric solutions causing major changes in electric power system architectures.
Abstract: The constant growth of air traffic, the demand for performance optimization, and the need for decreasing both operating and maintenance costs have encouraged the aircraft industry to move toward more electric solutions. As a result of this trend, electric power required on-board of aircraft has significantly increased through the years, causing major changes in electric power system architectures. Considering this scenario, this paper gives a review about the evolution of electric power generation systems in aircraft. The major achievements are highlighted and the rationale behind some significant developments discussed. After a brief historical overview of the early dc generators (both wind- and engine-driven), the reasons which brought the definitive passage to the ac generation, for larger aircraft, are presented and explained. Several ac generation systems are investigated with particular attention being focused on the voltage levels and the generator technology. Furthermore, examples of commercial aircraft implementing ac generation systems are provided. Finally, the trends toward modern generation systems are also considered giving prominence to their challenges and feasibility.
384 citations
TL;DR: In this article, a comprehensive review and critical discussion of state-of-the-art analytical techniques for optimal planning of renewable distributed generation is conducted, and a comparative analysis of analytical techniques is presented to show their suitability for distributed generation planning in terms of various optimization criteria.
327 citations
TL;DR: The concept of distributed power system virtual inertia, which can be implemented by grid-connected power converters, is proposed and validated through simulation and experimental results, which indicate that 12.5% and 50% improvements of the frequency nadir and rate of change of frequency can be achieved.
Abstract: Renewable energy sources (RESs), e.g., wind and solar photovoltaics, have been increasingly used to meet worldwide growing energy demands and reduce greenhouse gas emissions. However, RESs are normally coupled to the power grid through fast-response power converters without any inertia, leading to decreased power system inertia. As a result, the grid frequency may easily go beyond the acceptable range under severe frequency events, resulting in undesirable load-shedding, cascading failures, or even large-scale blackouts. To address the ever-decreasing inertia issue, this paper proposes the concept of distributed power system virtual inertia, which can be implemented by grid-connected power converters. Without modifications of system hardware, power system inertia can be emulated by the energy stored in the dc-link capacitors of grid-connected power converters. By regulating the dc-link voltages in proportional to the grid frequency, the dc-link capacitors are aggregated into an extremely large equivalent capacitor serving as an energy buffer for frequency support. Furthermore, the limitation of virtual inertia, together with its design parameters, is identified. Finally, the feasibility of the proposed concept is validated through simulation and experimental results, which indicate that 12.5% and 50% improvements of the frequency nadir and rate of change of frequency can be achieved.
312 citations
TL;DR: A state-of-the-art survey of the most relevant cyber security studies in power systems and a demonstration is provided to show how the proposed defense systems can be deployed to protect a power grid against cyber intruders.
308 citations
TL;DR: In this article, a hybrid ESS consisting of a battery and an ultracapacitor is proposed to achieve the power management of virtual synchronous generators (VSGs) in renewable energy sources.
Abstract: Renewable energy sources (RESs) have been extensively integrated into modern power systems to meet the increasing worldwide energy demand as well as reduce greenhouse gas emission. As a result, the task of frequency regulation previously provided by synchronous generators is gradually taken over by power converters, which serve as the interface between the power grid and RESs. By regulating power converters as virtual synchronous generators (VSGs), they can exhibit similar frequency dynamic response. However, unlike synchronous generators, power converters are incapable of absorbing/delivering any kinetic energy, which necessitates extra energy storage systems (ESSs). Nonetheless, the implementation and coordination control of ESSs in VSGs have not been investigated by previous research. To fill this research gap, this letter proposes a hybrid ESS (HESS) consisting of a battery and an ultracapacitor to achieve the power management of VSGs. Through proper control, the ultracapacitor automatically tackles the fast-varying power introduced by inertia emulation while the battery implements the remaining parts of a VSG and only compensates for relatively long-term power fluctuations with slow dynamics. In this way, the proposed HESS allows reduction of the battery power fluctuations along with its changing rate. Finally, experimental results are presented to validate the proposed concept.
293 citations
TL;DR: The basic functionality of PyPSA is described, including the formulation of the full power flow equations and the multi-period optimisation of operation and investment with linear power flow equation.
Abstract: Python for Power System Analysis (PyPSA) is a free software toolbox for simulating and optimising modern electrical power systems over multiple periods. PyPSA includes models for conventional generators with unit commitment, variable renewable generation, storage units, coupling to other energy sectors, and mixed alternating and direct current networks. It is designed to be easily extensible and to scale well with large networks and long time series. In this paper the basic functionality of PyPSA is described, including the formulation of the full power flow equations and the multi-period optimisation of operation and investment with linear power flow equations. PyPSA is positioned in the existing free software landscape as a bridge between traditional power flow analysis tools for steady-state analysis and full multi-period energy system models. The functionality is demonstrated on two open datasets of the transmission system in Germany (based on SciGRID) and Europe (based on GridKit). Funding statement: This research was conducted as part of the CoNDyNet project, which is supported by the German Federal Ministry of Education and Research under grant no. 03SF0472C. The responsibility for the contents lies solely with the authors
288 citations
TL;DR: Simulation results showed that both of the step length control and learning process involvement techniques improve the performance of P2P energy sharing mechanisms with moderate ramping/learning rates and showed that P2p energy sharing has the potential to bring both economic and technical benefits for Great Britain.
TL;DR: An adaptive event- triggering LFC scheme is presented, where the event-triggering threshold can be dynamically adjusted to save more limited network resources, while preserving the desired control performance.
Abstract: Load frequency control (LFC) is a very important method to keep the power systems stable and secure. However, due to the introduction of communication networks in multi-area power systems, the traditional LFC method is not effective again. This motivates us to investigate an adaptive event-triggering ${H}_{\infty }$ LFC scheme for multi-area power systems. Compared with the existing time-invariant event-triggering communication scheme, an adaptive event-triggering communication scheme is presented, where the event-triggering threshold can be dynamically adjusted to save more limited network resources, while preserving the desired control performance. Compared with the existing emulation-based method, where the controller must be known a priori , the stability and stabilization criteria derived in this work can provide a tradeoff to balance the required communication resources and the desired control performance. The effectiveness of the proposed method is verified by two numerical examples.
TL;DR: This paper presents a review on recent developments of control technologies and power management strategies proposed for AC ship microgrids.
Abstract: At sea, the electrical power system of a ship can be considered as an islanded microgrid. When connected to shore power at berth, the same power system acts as a grid connected microgrid or an extension of the grid. Therefore, ship microgrids show some resemblance to terrestrial microgrids. Nevertheless, due to the presence of large dynamic loads, such as electric propulsion loads, keeping the voltage and frequency within a permissible range and ensuring the continuity of supply are more challenging in ship microgrids. Moreover, with the growing demand for emission reductions and fuel efficiency improvements, alternative energy sources and energy storage technologies are becoming popular in ship microgrids. In this context, the integration of multiple energy sources and storage systems in ship microgrids requires an efficient power management system (PMS). These challenging environments and trends demand advanced control and power management solutions that are customized for ship microgrids. This paper presents a review on recent developments of control technologies and power management strategies proposed for AC ship microgrids.
TL;DR: The proposed CAPMS is successful in regulating the dc and ac bus voltages and frequency stably, controlling the voltage and power of each unit flexibly, and balancing the power flows in the systems automatically under different operating circumstances, regardless of disturbances from switching operating modes, fluctuations of irradiance and temperature, and change of loads.
Abstract: Battery storage is usually employed in photovoltaic (PV) system to mitigate the power fluctuations due to the characteristics of PV panels and solar irradiance. Control schemes for PV-battery systems must be able to stabilize the bus voltages as well as to control the power flows flexibly. This paper proposes a comprehensive control and power management system (CAPMS) for PV-battery-based hybrid microgrids with both ac and dc buses, for both grid-connected and islanded modes. The proposed CAPMS is successful in regulating the dc and ac bus voltages and frequency stably, controlling the voltage and power of each unit flexibly, and balancing the power flows in the systems automatically under different operating circumstances, regardless of disturbances from switching operating modes, fluctuations of irradiance and temperature, and change of loads. Both simulation and experimental case studies are carried out to verify the performance of the proposed method.
TL;DR: A ridgelet transform is applied to a wind signal to decompose it into sub-signals and the output of ridgelettransform is considered as input of new feature selection to identify the best candidates to be used as the forecast engine input.
TL;DR: This paper presents a comprehensive literature survey on the topic of LFC, and investigates the used LFC models for diverse configurations of power systems and proposes proposed control strategies for LFC for both conventional and future smart power systems.
Abstract: Power systems are the most complex systems that have been created by men in history To operate such systems in a stable mode, several control loops are needed Voltage frequency plays a vital role in power systems which need to be properly controlled To this end, primary and secondary frequency control loops are used to control the frequency of the voltage in power systems Secondary frequency control, which is called Load Frequency Control (LFC), is responsible for maintaining the frequency in a desirable level after a disturbance Likewise, the power exchanges between different control areas are controlled by LFC approaches In recent decades, many control approaches have been suggested for LFC in power systems This paper presents a comprehensive literature survey on the topic of LFC In this survey, the used LFC models for diverse configurations of power systems are firstly investigated and classified for both conventional and future smart power systems Furthermore, the proposed control strategies for LFC are studied and categorized into different control groups The paper concludes with highlighting the research gaps and presenting some new research directions in the field of LFC
TL;DR: In this article, the optimal operation of the integrated gas and electrical power system with bidirectional energy conversion is studied. But the authors focus on the optimal operating of the system considering the different response times of the gas and power systems, the transient gas flow and steady-state power flow are combined to formulate the dynamic optimal energy flow in the integrated GA and power system.
Abstract: This paper focuses on the optimal operation of the integrated gas and electrical power system with bidirectional energy conversion. Considering the different response times of the gas and power systems, the transient gas flow and steady-state power flow are combined to formulate the dynamic optimal energy flow in the integrated gas and power systems. With proper assumptions and simplifications, the problem is transformed into a single-stage linear programming to obtain the optimal operation strategy for both gas and power systems. Simulation on the test case illustrates the success of the modeling and the beneficial roles of the power-to-gas are analyzed. The proposed model can be used in the decision support for both planning and operation of the coordinated natural gas and electrical power systems.
TL;DR: A mixed-integer linear programming model is formulated for PEV fast-charging station planning considering both transportation and electrical constraints based on CFRLM, which can be solved by deterministic branch-and-bound methods.
Abstract: Plug-in electric vehicle (PEV) charging stations couple future transportation systems and power systems. That is, PEV driving and charging behavior will influence the two networks simultaneously. This paper studies optimal planning of PEV fast-charging stations considering the interactions between the transportation and electrical networks. The geographical targeted planning area is a highway transportation network powered by a high voltage distribution network. First, we propose the capacitated-flow refueling location model (CFRLM) to explicitly capture PEV charging demands on the transportation network under driving range constraints. Then, a mixed-integer linear programming model is formulated for PEV fast-charging station planning considering both transportation and electrical constraints based on CFRLM, which can be solved by deterministic branch-and-bound methods. Numerical experiments are conducted to illustrate the proposed planning method. The influences of PEV population, power system security operation constraints, and PEV range are analyzed.
TL;DR: Contrary to previous static approaches to quantify Energy Flexibility, the dynamic nature of the Flexibility Function enables a Flexibility Index, which describes to which extent a building is able to respond to the grid’s need for flexibility.
TL;DR: In this paper, the authors investigated the impact of the EV charging station loads on the voltage stability, power losses, reliability indices, as well as economic losses of the distribution network on the IEEE 33 bus test system representing a standard radial distribution network.
Abstract: Recent concerns about environmental pollution and escalating energy consumption accompanied by the advancements in battery technology have initiated the electrification of the transportation sector. With the universal resurgence of Electric Vehicles (EVs) the adverse impact of the EV charging loads on the operating parameters of the power system has been noticed. The detrimental impact of EV charging station loads on the electricity distribution network cannot be neglected. The high charging loads of the fast charging stations results in increased peak load demand, reduced reserve margins, voltage instability, and reliability problems. Further, the penalty paid by the utility for the degrading performance of the power system cannot be neglected. This work aims to investigate the impact of the EV charging station loads on the voltage stability, power losses, reliability indices, as well as economic losses of the distribution network. The entire analysis is performed on the IEEE 33 bus test system representing a standard radial distribution network for six different cases of EV charging station placement. It is observed that the system can withstand placement of fast charging stations at the strong buses up to a certain level, but the placement of fast charging stations at the weak buses of the system hampers the smooth operation of the power system. Further, a strategy for the placement of the EV charging stations on the distribution network is proposed based on a novel Voltage stability, Reliability, and Power loss (VRP) index. The results obtained indicate the efficacy of the VRP index.
TL;DR: In this paper, a sequential service restoration (SSR) framework is proposed to generate restoration solutions for distribution systems and micro-grids in the event of large-scale power outages.
Abstract: The resilience and reliability of modern power systems are threatened by increasingly severe weather events and cyber-physical security events. An effective restoration methodology is desired to optimally integrate emerging smart grid technologies and pave the way for developing self-healing smart grids. In this paper, a sequential service restoration (SSR) framework is proposed to generate restoration solutions for distribution systems and microgrids in the event of large-scale power outages. The restoration solution contains a sequence of control actions that properly coordinate switches, distributed generators, and switchable loads to form multiple isolated microgrids. The SSR can be applied for three-phase unbalanced distribution systems and microgrids and can adapt to various operation conditions. Mathematical models are introduced for three-phase unbalanced power flow, voltage regulators, transformers, and loads. The SSR problem is formulated as a mixed-integer linear programming model, and its effectiveness is evaluated via the modified IEEE 123 node test feeder.
TL;DR: Six decomposition coordination algorithms are studied, including analytical target cascading, optimality condition decomposition, alternating direction method of multipliers, auxiliary problem principle, consensus+innovations, and proximal message passing to solve the optimal power flow (OPF) problem in electric power systems.
Abstract: This paper reviews distributed/decentralized algorithms to solve the optimal power flow (OPF) problem in electric power systems. Six decomposition coordination algorithms are studied, including analytical target cascading, optimality condition decomposition, alternating direction method of multipliers, auxiliary problem principle, consensus+innovations, and proximal message passing. The basic concept, the general formulation, the application for dc-OPF, and the solution methodology for each algorithm are presented. We apply these six decomposition coordination algorithms on a test system, and discuss their key features and simulation results.
TL;DR: A comprehensive study of power quality in power systems, including the systems with dc and renewable sources is done, and power quality monitoring techniques and possible solutions of the power quality issues for the power systems are elaborately studied.
Abstract: This paper discusses the power quality issues for distributed generation systems based on renewable energy sources, such as solar and wind energy. A thorough discussion about the power quality issues is conducted here. This paper starts with the power quality issues, followed by discussions of basic standards. A comprehensive study of power quality in power systems, including the systems with dc and renewable sources is done in this paper. Power quality monitoring techniques and possible solutions of the power quality issues for the power systems are elaborately studied. Then, we analyze the methods of mitigation of these problems using custom power devices, such as D-STATCOM, UPQC, UPS, TVSS, DVR, etc., for micro grid systems. For renewable energy systems, STATCOM can be a potential choice due to its several advantages, whereas spinning reserve can enhance the power quality in traditional systems. At Last, we study the power quality in dc systems. Simpler arrangement and higher reliability are two main advantages of the dc systems though it faces other power quality issues, such as instability and poor detection of faults.
TL;DR: An innovative method employing the weighted Gaussian process regression approach is proposed, such that data samples with higher outlier potential have a low weight, and the results exhibit higher estimation accuracy.
Abstract: Photovoltaic (PV) power is volatile in nature and raises the level of uncertainty in power systems. PV power forecasting is an important measure to solve this problem. It helps to improve the reliability and reduces the generation cost. Advances in computer technology and sensors make the numeric modeling methods a hotspot in the field of PV power forecasting. However, data modeling methods strongly rely on the accuracy of measurement data. Unavoidable outliers in the measured meteorological data have an adverse effect on the model due to their heteroscedasticity. Although many studies can be found focusing on outlier detection, only a few have incorporated outlier detection with regression models. In this study, an innovative method employing the weighted Gaussian process regression approach is proposed, such that data samples with higher outlier potential have a low weight. A density-based local outlier detection approach is introduced to compensate the deterioration of Euclidean distance for high-dimensional data. A novel concept of the degree of nonlinear correlation is incorporated to compute the contribution of every individual data attribute. Effectiveness of the proposed method is demonstrated by performing an experimental analysis and making comparisons with other typical data-based approaches, and the results exhibit higher estimation accuracy.
TL;DR: A new false data injection attack detection mechanism for ac state estimation that can effectively capture inconsistency by analyzing temporally consecutive estimated system states using wavelet transform and deep neural network techniques is proposed.
Abstract: State estimation is critical to the operation and control of modern power systems. However, many cyber-attacks, such as false data injection attacks, can circumvent conventional detection methods and interfere the normal operation of grids. While there exists research focusing on detecting such attacks in dc state estimation, attack detection in ac systems is also critical, since ac state estimation is more widely employed in power utilities. In this paper, we propose a new false data injection attack detection mechanism for ac state estimation. When malicious data are injected in the state vectors, their spatial and temporal data correlations may deviate from those in normal operating conditions. The proposed mechanism can effectively capture such inconsistency by analyzing temporally consecutive estimated system states using wavelet transform and deep neural network techniques. We assess the performance of the proposed mechanism with comprehensive case studies on IEEE 118- and 300-bus power systems. The results indicate that the mechanism can achieve a satisfactory attack detection accuracy. Furthermore, we conduct a preliminary sensitivity test on the control parameters of the proposed mechanism.
TL;DR: Two constraint-based iterative search algorithms are proposed for optimal sizing of the wind turbine, solar photovoltaic and the battery energy storage system (BESS) in the grid-connected configuration of a microgrid to avoid over- and under-sizing.
Abstract: Higher cost and stochastic nature of intermittent renewable energy (RE) resources complicate their planning, integration and operation of electric power system. Therefore, it is critical to determine the appropriate sizes of RE sources and associated energy storage for efficient, economic and reliable operation of electric power system. In this study, two constraint-based iterative search algorithms are proposed for optimal sizing of the wind turbine (WT), solar photovoltaic (PV) and the battery energy storage system (BESS) in the grid-connected configuration of a microgrid. The first algorithm, named as sources sizing algorithm, determines the optimal sizes of RE sources while the second algorithm, called as battery sizing algorithm, determines the optimal capacity of BESS. These algorithms are mainly based upon two key essentials, i.e. maximum reliability and minimum cost. The proposed methodology aims to avoid over- and under-sizing by searching every possible solution in the given search space. Moreover, it considers the forced outage rates of PV, WT and utilisation factor of BESS which makes it more realistic. Simulation results depict the effectiveness of the proposed approach.
11 Jun 2018
TL;DR: This work proposes PowerModels, an open-source platform for comparing power flow formulations, and provides a brief introduction to the design, validates its implementation, and demonstrates its effectiveness with a proof-of-concept study analyzing five different formulations of the Optimal Power Flow problem.
Abstract: In recent years, the power system research community has seen an explosion of novel methods for formulating and solving power network optimization problems. These emerging methods range from new power flow approximations, which go beyond the traditional DC power flow by capturing reactive power, to convex relaxations, which provide solution quality and runtime performance guarantees. Unfortunately, the sophistication of these emerging methods often presents a significant barrier to evaluating them on a wide variety of power system optimization applications. To address this issue, this work proposes PowerModels, an open-source platform for comparing power flow formulations. From its inception, PowerModels was designed to streamline the process of evaluating different power flow formulations on shared optimization problem specifications. This work provides a brief introduction to the design of PowerModels, validates its implementation, and demonstrates its effectiveness with a proof-of-concept study analyzing five different formulations of the Optimal Power Flow problem.
TL;DR: In this paper, the authors developed a transient stability assessment system based on the long short-term memory network by proposing a temporal self-adaptive scheme, which aims to balance the trade-off between assessment accuracy and response time.
Abstract: Online identification of postcontingency transient stability is essential in power system control, as it facilitates the grid operator to decide and coordinate system failure correction control actions Utilizing machine learning methods with synchrophasor measurements for transient stability assessment has received much attention recently with the gradual deployment of wide-area protection and control systems In this paper, we develop a transient stability assessment system based on the long short-term memory network By proposing a temporal self-adaptive scheme, our proposed system aims to balance the trade-off between assessment accuracy and response time, both of which may be crucial in real-world scenarios Compared with previous work, the most significant enhancement is that our system learns from the temporal data dependencies of the input data, which contributes to better assessment accuracy In addition, the model structure of our system is relatively less complex, speeding up the model training process Case studies on three power systems demonstrate the efficacy of the proposed transient stability as sessment system
TL;DR: In this paper, the stochastic optimal operation for the micro integrated electric power, natural gas, and heat delivery system (IPGHS) is investigated for the high-efficient utilization of multitype energy systems.
Abstract: Integrated energy system is important for the high-efficient utilization of multitype energy systems. In this paper, the stochastic optimal operation is investigated for the micro integrated electric power, natural gas, and heat delivery system (IPGHS). First, a low-carbon micro-IPGHS is proposed with the comprehensive consideration of renewable generation, carbon-capture-based power-to-gas technology, and the combined power and heat units. Second, a scenario-based optimal operation model for micro-IPGHS is proposed to handle uncertainties in energy demand and renewable generation. In the proposed model, energy transactions between micro-IPGHS and upstream energy systems as well as constraints for battery storage, natural gas storage, and heat storage systems are considered. Finally, a case study is used for the proposed low-carbon micro-IPGHS to validate the optimal stochastic operation approach. The proposed integrated system can effectively utilize the variable clean energy for optimizing the delivery of the green operation in micro-IPGHS.
TL;DR: HV bulk transport links, storage technologies and the so-called digital revolution are taking a leading role in different parts of the world for the development of a deep decarbonization of the electricity sector, of new energy business models at distribution level and of new power distribution architectures.
Abstract: This paper addresses the impact over key power infrastructures of the three main drivers for change of these times: Decarbonization, Digitalization and Decentralization. The three phenomena, according to prominent observers, are affecting all fields of our lives but, in the literature, it is difficult to find an analysis of their impact on electrical power systems. The framework proposed in this paper, based on the main power systems evolution models proposed by CIGRE, uses data from open databases and tries to find out general guidelines for power systems development at a worldwide level. Taking as reference the European and COP21 environmental objectives and beyond, the technological evolution of some key enabling technologies is explored. What emerges is that HV bulk transport links, storage technologies and the so-called digital revolution are taking a leading role in different parts of the world for the development of a deep decarbonization of the electricity sector, of new energy business models at distribution level and of new power distribution architectures.