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This article focuses on carbon emission quota allocation regulations and their impact on investments in low-carbon technology within the electric power industry, as well as other enterprises’ decisions and profits under peak-valley price policy and cap-and-trade mechanism. An electricity supply-chain model is constructed in a Stackelberg game, which comprises an electricity-generating enterprise (leader) and an electricity retailer (follower). The backward-induction method is used to obtain the investment in low-carbon technology and electricity price under three scenarios of carbon emission quota allocation regulations: no regulation, grandfathering allocation, and benchmarking allocation. The main results are: first, benchmarking allocation always results in higher investment in low-carbon technology, but generates greater total carbon emissions than grandfathering allocation under most conditions; and second, under benchmarking allocation, as the unit carbon emission quota increases, total carbon emissions first increase and then decrease. Therefore, benchmarking allocation is the better regulation for the low-carbon technology investment. Moreover, benchmarking allocation is also the better regulation for total carbon emissions with a higher unit carbon quota or a higher carbon price.

The renewable energy-based hydrogen production can lead to the integrated electric power and hydrogen system (IPHS) and offer a pathway to a sustainable energy utilization. Hydrogen is mainly transported via hydrogen tube trailers (HTs), making the hydrogen energy system (HES) operation quite different from those of other energy technologies. This paper proposes an optimal IPHS operation strategy which utilizes HT for transportation. The proposed strategy coordinates hydrogen generation, transportation, and storage stages considering constrained operations of electric power system (EPS), transportation system, and variable renewable energy. The proposed solution method is based on the alternating direction method of multipliers (ADMM) in which HES and EPS constraints are managed individually and the respective solutions are coordinated accordingly. The case studies using the modified IEEE-RTS79 have verified the validity of the proposed IPHS model and its solution method and confirmed the necessity of considering HES in enhancing the EPS operation. The synergies between EPS and HES are studied via numerical examples and the impact of the flexibilities in hydrogen generation, transportation and demand are highlighted.

Situation awareness (SA) has been recognized as a critical guarantee for the stable and secure operation of electric power systems, especially under complex uncertainties after renewable energy integration. In this article, an artificial-intelligence-powered solution is presented to reach a full realization of SA covering perception, comprehension, and prediction, the last of which is more advanced but challenging and hence has not been discussed in any literature before. A novel SA model is proposed by aggregating two powerful deep learning structures: convolutional neural network (CNN) and long short-term memory (LSTM) recurrent neural network. The proposed CNN-LSTM model has superiority to achieve collaborative data mining on spatiotemporal measurement data, i.e., to learn both spatial and temporal features simultaneously from phasor measurement units data. Two functional branches are designed within the SA model: a contingency locator to detect the exact fault location at present and a stability predictor to predict stability status of the system in the future. Test results have shown high performance (accuracy) of the model even on a low level of data adequacy. The proposed SA model can promisingly facilitate very fast postfault actions by the system operators to prevent the power system from any unstable operational status.

International audience; This paper proposes a new framework for the operational reliability evaluation of integrated electric power-gas networks (IEPGNs). First, a novel approach for modeling the failure modes of natural gas pipelines is presented. This approach utilizes the concept of virtual nodes and employs a gas release rate model to consider the pinhole, hole, and rupture failure modes of pipelines. Thereafter, a four-state Markov model for natural gas-fired generators (NGFGs) with dual-fuel capabilities is proposed. The area risk method is extended to include the proposed reliability models, and the partial reliability indices of the area risk method are evaluated using a non-sequential Monte Carlo simulation (NSMCS). A nonlinear optimization model is also proposed to calculate electric and gas load curtailments for each system state in NSMCS. This model is linearized to obtain a mixed-integer linear programming (MILP) model for reducing the computational burden. The computational performance of NSMCS is further improved by adopting cross entropy (CE)-based importance sampling (IS). Finally, the efficacy of the proposed framework is demonstrated on three test systems. Case studies validate the importance of considering the proposed reliability models of IEPGNs for operational reliability evaluation. The impacts of operational strategies on the operational reliability indices are also demonstrated.

Fault current limiters (FCLs) are promising fault current management measures and one of the most economical solutions to today's electric power systems challenges that could have potential for greater use in power systems. To achieve highest efficiency with lowest cost, several efforts have been proposed in literature to provide the optimal location, number, type, size, and parameters setting of FCL. In this article, the existing research works on FCL integration are reviewed from viewpoint of their used design variables, network structures, FCL types, constraints, protection devices, and objective functions. The main aim of this article is to provide a guide on the available FCL placement works, to aid electrical engineers and researchers.

New green energy policies are encouraging district heating (DH) companies to exploit heat production by power-to-heat (P2H) assets, e.g., heat pumps (HPs) and electric boilers (EBs). Therefore, they are shifting to exploit these assets in their distribution network besides the traditional central combined heat and power (CHP) units in their production chain. It will lead to an increasingly complex interaction between DH networks and electricity markets. This article proposes an economic model predictive control (EMPC) framework to investigate how DH companies can manage this extra complexity and if they can contribute in an efficient way towards balancing the production and consumption of electric power in smart grids through playing in the reserve capacity markets. The framework is applied to a real world case study and the results are discussed. The results show the new paradigm increases operation costs. However, reserving a part of the capacity for playing in reserve capacity market will not only make up for the extra costs to some extent, but also improve the capability of providing regulating power.

Simultaneous over 40-W electric power and optical data transmission using an optical fiber is demonstrated for optically powered remote antenna units in future mobile communication networks. In this article, to further increase the delivered electric power by power-over-fiber link using a double-clad fiber, we improve the link design to extract a higher feed light power from the double-clad fiber output. Furthermore, to increase the electric power for driving remote antenna units, we employ a specially customized photovoltaic power converter that directly converts optical power into electric power. The photovoltaic power converter can input a feed light with power of over 20 W and has a high optical-to-electrical conversion efficiency of over 50%. As a result, the combination of the improved power-over-fiber link design and the use of the photovoltaic power converter successfully achieves the electric power delivery of up to 43.7 W. This is the highest electric power delivery demonstration by power-over-fiber with optical data signals using a single optical fiber, to the best of the authors’ knowledge.

Wildfires pose a significant challenge to the natural and the built environments, as well as the safety and economic wellbeing of the communities residing in wildfire-prone areas. The electric power grid is specifically among the built environments most affected by, and contributing to, wildfires. In this paper, we propose a three lines of defense (3LD) framework for wildfire risk management in electric power infrastructure and review the literature from this lens. An overview of the physics and phenomenology of the wildfires as it relates to power grids is presented, and the logic for the proposed 3LD framework is discussed. The reviewed literature based on the 3LD theme includes the most relevant and emerging research work on wildfire prevention as the first line of defense, wildfire mitigation and proactive response as the second line of defense, and wildfire recovery preparedness as the third line of defense. This study reveals that while the state of the art, to a large extent, stands comprehensive in various aspects of power system resilience and wildfire risk management, there is a gap in the literature in addressing this emerging risk in a holistic, interdisciplinary approach.

Current distribution system outage management is to separately use digital relays at the substation for estimation of fault location and use meter information to infer the activated protection device. The lack of holistic utilization of available data in the distribution operating center results in lost opportunities for accurate fault diagnosis. To solve this issue, this study proposes a data-driven probabilistic fault location methodology based on comprehensive sensing measurement from digital relays at substations, Intelligent Electric Devices (IEDs) along primary feeders, SCADA sensors in the feeder circuit, and smart meters at customers’ premises. Statistics of historical fault location accuracies by digital relays and IEDs are used to estimate fault location errors with probability in real time. Multiple-hypothesis analysis is implemented to handle the uncertainties from SCADA sensors and smart meters. The spatial correlation between the potential fault location and collected sensor data is modeled as a mixed integer linear programming (MILP) problem. By solving the proposed optimization for generated hypotheses, a list of potential fault locations with possibilities is provided to system operators in decision-making for facilitated fault isolation and service restoration. Simulation results with a utility feeder validate the efficacy of the proposed approach for fault diagnosis.

It is an open question whether or not the sharing economy model—utilizing unused for-profit private resources by sharing them for public use through a digital intermediary—could be adopted for public utility and service delivery in the developing world. This article addresses the question using the electric power sector in India as a case study. Six major components of the sharing economy are taken up for analysis: suppliers/service providers; infrastructure facilities; customers; public policy/regulations; platform; and pricing structure. This article discusses the status, challenges, and possible future directions.

CubeSats have been widely used for space research due to lower cost and faster development. The electric power system (EPS) is one of the key subsystems of CubeSat which powers all the other subsystems. One of the important steps in the EPS design is the selection of EPS architecture which should be done considering overall efficiency, battery size, reliability, and simplicity of control. In the literature, a general comparison between different architectures is performed without considering the mission parameters, power generation profile, and load profile based on the operational modes. Thus, the best possible EPS architecture may not be selected in the design phase. Therefore, the main objective of this article is to develop a systematic methodology to compare various peak power tracking EPS architectures of CubeSat in terms of orbital efficiency for all possible modes of operation, component count, reliability, and battery size to meet the required number of cycles of charge/discharge for the given mission duration. The proposed methodology has been demonstrated using the real data and scenarios of MYSAT-1, which is a 1U CubeSat developed and launched by Khalifa University. The results demonstrate that EPS architecture with series-connected maximum power tracking converters for solar panels and unregulated dc-bus has the highest efficiency for all operating modes, lower component count, higher reliability, and minimum battery capacity or longer lifetime for the same battery specifications.

This work considers the operation of the electric power system (EPS) in coordination with the water distribution system (WDS) during extreme operating condition scenarios. The interdependencies are captured by modeling the main dependency of the WDS on the EPS, power for WDS pumps, as well as the main dependency of the EPS on the WDS, the need for cooling water for conventional thermoelectric generation, in simulation. The extreme scenarios include studying the effect of droughts (water shortages) in the WDS and electrical power outages of WDS pumping stations in the EPS. Appropriate network models and mathematical formulations for optimizing control of the operation in the power system with consideration of the present operating conditions in the WDS are presented. These include a novel optimal power flow model for unit dispatch, a model for systematically derating thermoelectric generation during periods of droughts as well as a method for adjusting the cooling water demands of that generation within the WDS. The implementation of these policies is seen to increase the duration for which conventional thermoelectric generation is available for dispatch during such extreme conditions.

The electricity demand has grown continuously in recent years, raising the necessity to expand generation sources, distribution networks, and equipment efficiency. In addition, it is necessary to attend sustainable development in a conciliated manner. Applications involving the intelligent management of the distributed networks have increased to achieve a balance between growth and sustainability. In this context, this article presents the development of an Intelligent Electric Power Management System (IEPMS) for the economic maximisation of a photovoltaic system applied to a prosumer residential unit without storage in Brazil. Using historical meteorological data and a heuristic to simulate energy use habits, the IEPMS forecasts both generation and demand in 24 hours. From the projections, an optimisation problem was built and solved using the genetic algorithm technique to find the most economical moments for driving loads. This model aims to reach the lowest daily cost of electricity, considering the return (sale) of unused energy to the power distribution company. The validation of the IEPMS considered four usage patterns, integrating 26 scenarios, those composed by the (i) flexibility; (ii) type of tariff; and (iii) hit rates provided by the climate forecasting method proposed for the system. As a result, the IEPMS savings considering the white tariff were 34.72% for one year, assuming full-time external work usage. Additionally, it was possible to identify in all scenarios that the proposed method’s performance was not less than 97%, measured through the relative error among distinct hit rates of the evaluated climatic forecast.

As smart load adoption grows on the electric power system, potential for losing load diversity increases, possibly in ways that impact system stability. Cloud computing resources are able to coordinate large amounts of behind-the-meter loads and resources. Inadvertent or malicious actions could potentially result in gigawatts of load, distributed across large regions, acting nearly simultaneously. We study the resulting impacts of such a perturbation, which were previously recognized, with improved fidelity and granularity using a physically-based power system and demand model. The ResStock tool was used to calculate residential air conditioning load at more than 3,000 locations across the Western Interconnection, corresponding in time to heavy summer and light spring loading. Under an assumption that one cloud platform managed smart thermostats controlling 10%, 15%, or 20% of residential air-conditioning, calculated load steps could be injected into Positive Sequence Load Flow dynamic simulations. These load-driven effects were coupled with two classes of distributed generation ride-through to evaluate the potential for cascading outages. We found frequency deviations in the spring case far exceed the credible contingency event, leading to widespread distributed generation loss, while voltage depressions during the summer loading lead to widespread distributed generation loss and system separation.

This article is reprinted from the “Scanning the Past” column in the April 2018 issue of Proceedings of the IEEE (vol. 105, no. 4, pp. 768–778). I am pleased that Dr. Adam Allerhand provided permission to republish this excellent article with refreshing viewpoints on the history of early electrification and electric power transmission. While researching these topics, I discovered his article and knew it would be a welcome addition to the IEEE Industry Applications Magazine “History” column.

The fundamental requirements for electric power-system operations are the same regardless of the system size. While specific operational implementations will change based on the measurements-regardless of whether the power system spans a continent or a single building, it is necessary to regulate the frequency and voltage, adjust generating unit outputs, and to protect equipment from electrical faults. In this way, the operational requirements of electric power systems exhibit fractal behaviors. Because of this operational characteristic, it is technically feasible to separate or segment any portion of an electric power system and operate it independently, if sufficient generating assets and control systems are present. However, the generating assets and controls necessary to implement this type of operation have not traditionally been deployed; centralized generation and control is currently the predominant operational paradigm. For example, the frequency is typically regulated at a regional level by balancing the generation at large central units with the consumption of power by end-use customers and system losses. Small changes in the frequency are used to coordinate the sharing of the load between generators on a regional level.

The object of this paper is to demonstrate the performance of a Ramp-Rejecting electric Power Prefilter (RRPP), which is a modification of the Conventional electric Power Prefilter (CPP) for a speed-PSS. The RRPP can markedly reduce the undesirable swings in generator voltage and reactive power output due to ramping of turbine power. Like the Dual Input Prefilter (DIP) of the integral-of-accelerating-power (IAP) PSS the RRPP (i) significantly attenuates the torsional modes; and (ii) enhances modal damping. Significantly, the RRPP does not require an externally-estimated rotor-speed input signal - which is an important requirement of the DIP. For example, using generator terminal variables for the DIP it is difficult to accurately estimate a rotor-speed input that covers a wide range of operating conditions and modal frequencies.