Abstract:The great challenges faced by modern power systems require a fresh look at the conventional operation paradigm. The significant challenges faced by modern power systems require an innovative method for the conventional operation paradigm. We claim that the decarbonization of the power grid and extensive electrification of numerous sectors of human activity can only be fostered by a self-adaptable and smart power grid that manifests similar qualities to those of the Internet. The Internet is constructed on a layered architecture that facilitates technology innovations and its intelligence is distributed throughout a hierarchy of networks. In this paper, the fundamental differences between the network data flows and power flows are examined, and the basic requirements for an innovative operation paradigm are highlighted. The current power grid is operated in a highly inflexible, centralized manner to meet increased security goals. A new highly flexible, distributed architecture can be realized by distributing the operation responsibility in smaller areas or even in grid components that can make autonomous decisions. The characteristics of such a power grid are presented, and the key features and advances for the on-going transition to a sustainable power system are identified. Finally, a case study on distributed voltage control is presented and discussed.

Abstract:Electricity is predicted to be the energy vector that will undergo major changes in the future, and a transition would be observed in the resources such as waste and residual biomass that we use to satisfy the energy demand. Therefore, this study aims to highlight the main economic and environmental performances of different biowaste-to-energy technologies for small-scale electricity generation by comparing the direct combustion of refined vegetable oil obtained from waste cooking oils (thermal pathway), anaerobic digestion of biowaste (biochemical pathway), and gasification of wood residues (thermochemical pathway). The economic analysis is mainly based on personal experiences in the energy sector and shows an overview of the performance in investment of combined heat and power (CHP) systems, ranging from 100 to 500 kW for a period of 20 years. The environmental assessment is conducted considering the life-cycle thinking approach using support from the openLCA software, product environmental footprint (PEF) database, and previous studies that have reported environmental inventory data from real industrial cases.

Abstract:The damping performance evaluation for electromechanical oscillations in power systems is crucial for the stable operation of modern power systems. In this paper, the connection between two commonly-used damping performance evaluation methods, i.e., the damping torque analysis (DTA) and energy flow analysis (EFA), are systematically examined and revealed for the better understanding of the oscillatory damping mechanism. First, a concept of the aggregated damping torque coefficient is proposed and derived based on DTA of multi-machine power systems, which can characterize the integration effect of the damping contribution from the whole power system. Then, the pre-processing of measurements at the terminal of a local generator is conducted for EFA, and a concept of the frequency-decomposed energy attenuation coefficient is defined to screen the damping contribution with respect to the interested frequency. On this basis, the frequency spectrum analysis of the energy attenuation coefficient is employed to rigorously prove that the results of DTA and EFA are essentially equivalent, which is valid for arbitrary types of synchronous generator models in multi-machine power systems. Additionally, the consistency between the aggregated damping torque coefficient and frequency-decomposed energy attenuation coefficient is further verified by the numerical calculation in case studies. The relationship between the proposed coefficients and the eigenvalue (or damping ratio) is finally revealed, which consolidates the application of the proposed concepts in the damping performance evaluation.

Abstract:Utility maximization is a major priority of prosumers participating in peer-to-peer energy trading and sharing (P2P-ETS). However, as more distributed energy resources integrate into the distribution network, the impact of the communication link becomes significant. We present a multi-commodity formulation that allows the dual-optimization of energy and communication resources in P2P-ETS. On one hand, the proposed algorithm minimizes the cost of energy generation and communication delay. On the other hand, it also maximizes the global utility of prosumers with fair resource allocation. We evaluate the algorithm in a variety of realistic conditions including a time-varying communication network with signal delay signal loss. The results show that the convergence is achieved in a fewer number of time steps than the previously proposed algorithms. It is further observed that the entities with a higher willingness to trade the energy acquire more satisfactions than others.

Abstract:The hidden failures generally exist in power systems and could give rise to cascading failures. Identification of hidden failures is challenging due to very low occurrence probabilities. This paper proposes a state-failure-network (SF-network) method to overcome the difficulty. The SF-network is formed by searching the failures and states guided by risk estimation indices, in which only the failures and states contributing to the blackout risks are searched and duplicated searches are avoided. Therefore, sufficient hidden failures can be obtained with acceptable computations. Based on the state and failure value calculations in the SF-network, the hidden failure critical component indices can be obtained to quantify the criticalities of the lines. The proposed SF-network method is superior to common sampling based methods in risk estimation accuracy. Besides, the state and failure value calculations in the SF-network used to re-estimate the risks after deployment of measures against hidden failures need shorter time in comparison with other risk re-estimation methods. The IEEE 14-bus and 118-bus systems are used to validate the method.

Abstract:Communication plays a vital role in incorporating smartness into the interconnected power system. However, historical records prove that the data transfer has always been vulnerable to cyber-attacks. Unless these cyber-attacks are identified and cordoned off, they may lead to black-out and result in national security issues. This paper proposes an optimal two-stage Kalman filter (OTS-KF) for simultaneous state and cyber-attack estimation in automatic generation control (AGC) system. Biases/cyber-attacks are modeled as unknown inputs in the AGC dynamics. Five types of cyber-attacks, i.e., false data injection (FDI), data replay attack, denial of service (DoS), scaling, and ramp attacks, are injected into the measurements and estimated using OTS-KF. As the load variations of each area are seldom available, OTS-KF is reformulated to estimate the states and outliers along with the load variations of the system. The proposed technique is validated on the benchmark two-area, three-area, and five-area power system models. The simulation results under various test conditions demonstrate the efficacy of the proposed filter.

Abstract:Accurate regional wind power prediction plays an important role in the security and reliability of power systems. For the performance improvement of very short-term prediction intervals (PIs), a novel probabilistic prediction method based on composite conditional nonlinear quantile regression (CCNQR) is proposed. First, the hierarchical clustering method based on weighted multivariate time series motifs (WMTSM) is studied to consider the static difference, dynamic difference, and meteorological difference of wind power time series. Then, the correlations are used as sample weights for the conditional linear programming (CLP) of CCNQR. To optimize the performance of PIs, a composite evaluation including the accuracy of PI coverage probability (PICP), the average width (AW), and the offsets of points outside PIs (OPOPI) is used to quantify the appropriate upper and lower bounds. Moreover, the adaptive boundary quantiles (ABQs) are quantified for the optimal performance of PIs. Finally, based on the real wind farm data, the superiority of the proposed method is verified by adequate comparisons with the conventional methods.

Abstract:Renewable energy production has been surging around the world in recent years. To mitigate the increasing uncertainty and intermittency of the renewable generation, proactive demand response algorithms and programs are proposed and developed to further improve the utilization of load flexibility and increase the efficiency of power system operation. One of the biggest challenges to efficient control and operation of demand response resources is how to forecast the baseline electricity consumption and estimate the load impact from demand response resources accurately. In this paper, we propose a mixed effect segmented regression model and a new robust estimate for forecasting the baseline electricity consumption in Southern California, USA, by combining the ideas of random effect regression model, segmented regression model, and the least trimmed squares estimate. Since the log-likelihood of the considered model is not differentiable at breakpoints, we propose a new backfitting algorithm to estimate the unknown parameters. The estimation performance of the new estimation procedure has been demonstrated with both simulation studies and the real data application for the electric load baseline forecasting in Southern California.

Abstract:Wind energy has posed new challenges in both transmission and distribution systems owing to its uncertain nature. The effect of wind turbines (WTs) on the actual payments charged by upstream networks to distribution system companies (DISCOs) is one challenge. Moreover, when the grid-connected inverters of WT operate in the lead or lag modes, WTs absorb or inject reactive power from the system. This paper proposes an approach to assess the importance of operation modes of WTs to minimize the costs by DISCOs in the presence of system uncertainties. Accordingly, an optimization problem is formulated to minimize the costs to DISCO by determining the optimal locations and sizes of WTs in optimally reconfigured distribution systems. In addition, an improved vector-based swarm optimization (IVBSO) algorithm is proposed because it is highly suitable for vector-based problems. Two distribution systems are used in the simulations to evaluate the proposed algorithm. Firstly, the capabilities of the IVBSO algorithm to determine better solutions over other heuristic algorithms are confirmed using the IEEE 33-bus test system. Secondly, the BijanAbad distribution system (BDS) is used to demonstrate the effectiveness of the proposed optimization problem. Accordingly, the distribution system model, cumulative distribution function of wind speed, and load profile are all extracted from the actual data of the BijanAbad region. Finally, the optimization problem is applied to BDS in both the lead and lag modes of WTs. Results indicate that the total costs of DISCO are lower when WTs operate in the lag mode than in the lead mode.

Abstract:The Energy Internet is a fundamental infrastructure for deploying green city applications, where energy saving and job acceleration are two critical issues to address. In contrast to existing approaches that focus on static metrics with the assumption of complete prior knowledge of resource information, both application-level properties and energy-level requirements are realized in this paper by jointly considering energy saving and job acceleration during job runtime. Considering the online environment of smart city applications, the main objective is transferred as an optimization problem with a model partition and function assignment. To minimize the energy cost and job completion time together, a green workload placement approach is proposed by using the multi-action deep reinforcement learning method. Evaluations with real-world applications demonstrate the superiority of this method over state-of-the-art methods.

Abstract:This study proposes an optimized model of a micro-energy network (MEN) that includes electricity and natural gas with integrated solar, wind, and energy storage systems (ESSs). The proposed model is based on energy hubs (EHs) and it aims to minimize operation costs and greenhouse emissions. The research is motivated by the increasing use of renewable energies and ESSs for secure energy supply while reducing operation costs and environment effects. A general algebraic modeling system (GAMS) is used to solve the optimal operation problem in the MEN. The results demonstrate that an optimal MEN formed by multiple EHs can provide appropriate and flexible responses to fluctuations in electricity prices and adjustments between time periods and seasons. It also yields significant reductions in operation costs and emissions. The proposed model can contribute to future research by providing a more efficient network model (as compared with the traditional electricity supply system) to scale down the environmental and economic impacts of electricity storage and supply systems on MEN operation.

Abstract:This paper investigates the power sharing and voltage regulation issues of islanded single-/three-phase microgrids (S/T-MGs) where both sources and loads are unbalanced and the presence of adversarial cyber-attacks against sensors of distributed generator (DG) units is considered. Firstly, each DG unit is modeled as a heterogeneous linear dynamic agent with disturbances caused by sources and loads, then the problem is formulated as a distributed containment control problem. After that, to guarantee satisfactory power sharing and voltage control performance asymptotically achieved for the S/T-MGs, an attack-resilient distributed secondary control approach is developed by designing a distributed adaptive observer. With this approach, the effect of the cyber-attacks can be neutralized to ensure system stability and preserve bounded voltage synchronization. Simulation results are presented to demonstrate the effectiveness of the proposed control approach.

Abstract:This paper proposes a decentralized demand management approach to reduce the energy bill of industrial park and improve its economic gains. A demand management model for industrial park considering the integrated demand response of combined heat and power (CHP) units and thermal storage is firstly proposed. Specifically, by increasing the electricity outputs of CHP units during peak-load periods, not only the peak demand charge but also the energy charge can be reduced. The thermal storage can efficiently utilize the waste heat provided by CHP units and further increase the flexibility of CHP units. The heat dissipation of thermal storage, thermal delay effect, and heat losses of heat pipelines are considered for ensuring reliable solutions to the industrial park. The proposed model is formulated as a multi-period alternating current (AC) optimal power flow problem via the second-order conic programming formulation. The alternating direction method of multipliers (ADMM) algorithm is used to compute the proposed demand management model in a distributed manner, which can protect private data of all participants while achieving solutions with high quality. Numerical case studies validate the effectiveness of the proposed demand management approach in reducing peak demand charge, and the performance of the ADMM-based decentralized computation algorithm in deriving the same optimal results of demand management as the centralized approach is also validated.

Abstract:Distribution networks are commonly used to demonstrate low-voltage problems. A new method to improve voltage quality is using battery energy storage stations (BESSs), which has a four-quadrant regulating capacity. In this paper, an optimal dispatching model of a distributed BESS considering peak load shifting is proposed to improve the voltage distribution in a distribution network. The objective function is to minimize the power exchange cost between the distribution network and the transmission network and the penalty cost of the voltage deviation. In the process, various constraints are considered, including the node power balance, single/two-way power flow, peak load shifting, line capacity, voltage deviation, photovoltaic station operation, main transformer capacity, and power factor of the distribution network. The big M method is used to linearize the nonlinear variables in the objective function and constraints, and the model is transformed into a mixed-integer linear programming problem, which significantly improves the model accuracy. Simulations are performed using the modified IEEE 33-node system. A typical time period is selected to analyze the node voltage variation, and the results show that the maximum voltage deviation can be reduced from 14.06% to 4.54%. The maximum peak-valley difference of the system can be reduced from 8.83 to 4.23 MW, and the voltage qualification rate can be significantly improved. Moreover, the validity of the proposed model is verified through simulations.

Abstract:To optimally control the energy storage system of the battery exposed to the volatile daily cycling load and electricity tariffs, a novel modification of a conventional model predictive control is proposed. The uncertainty of daily cycling load prompts the need to design a new cost function which is able to quantify the associated uncertainty. By modelling a probabilistic dependence among flow, load, and electricity tariffs, the expected cost function is obtained and used in the constrained optimization. The proposed control strategy explicitly incorporates the cycling nature of customer load. Furthermore, for daily cycling load, a fixed-end time and a fixed-end output problem are addressed. It is demonstrated that the proposed control strategy is a convex optimization problem. While stochastic and robust model predictive controllers evaluate the cost concerning model constraints and parameter variations. Also, the expected cost across the flow variations is considered. The density function of load probability improves load prediction over a progressive prediction horizon, and a nonlinear battery model is utilized.

Abstract:Electric vehicles (EVs) are widely deployed throughout the world, and photovoltaic (PV) charging stations have emerged for satisfying the charging demands of EV users. This paper proposes a multi-objective optimal operation method for the centralized battery swap charging system (CBSCS), in order to enhance the economic efficiency while reducing its adverse effects on power grid. The proposed method involves a multi-objective optimization scheduling model, which minimizes the total operation cost and smoothes load fluctuations, simultaneously. Afterwards, we modify a recently proposed multi-objective optimization algorithm of non-sorting genetic algorithm III (NSGA-III) for solving this scheduling problem. Finally, simulation studies verify the effectiveness of the proposed multi-objective operation method.

Abstract:Consecutive charging and discharging of storage devices (SDs) might deem beneficial from the perspective of short-term operation. However, it highly impacts the life span of the embedded battery and render restrictions on energy storage capacity. We investigate short-term and long-term constraints of SDs through a three-stage price-elastic approach to the optimal operation of small-scale SDs in smart houses. The first stage deals with data and scenario characterization where the data for determining short-term and long-term operation constraints of SD are acquired. Proper number of scenarios are generated to represent uncertain parameters such as long-term demand forecasting, daily load profile, electricity price, and photovoltaic (PV) generation. The second stage optimizes the long-term operation of SD using the envisioned scenarios subject to the long-term operation constraints and the installment costs of SDs. The outputs of this stage are two indicators referred to as price elasticity and price offset coefficients, which are used as the inputs for the third stage. The third stage is responsible for decision-making on short-term operation of SDs. The outputs of the second stage along with short-term forecasting for daily electricity price, daily load and daily PV generation are acquired. Based on the acquired data, proper price elasticity and price offset are determined for optimal operation. Comprehensive simulations are performed for different demand forecasting and electricity prices. Simulation results confirm the effectiveness of the proposed approach.

Abstract:Recent research findings indicate that the non-monotonic consumption of energy from lithium-ion (Li-ion) batteries results in a higher heat generation in electrical energy storage systems. During peak demands, a higher heat generation due to high discharging current increases the temperature from 80 °C to 120 °C, thereby resulting in thermal runaway. To address peak demands, an additional electrical energy storage component, namely supercapacitor (SC), is being investigated by various research groups. This paper provides insights into the capability of SCs in lightweight electric vehicles (EVs) to address peak demands using the worldwide harmonized light-duty driving test cycle (WLTC) driving profile in MATLAB/Simulink at different ambient temperatures. Simulation results indicate that temperature imposes a more prominent effect on Li-ion batteries compared with SCs under peak demand conditions. The effect of the discharging rate limit on the Li-ion battery current is studied. The result shows that SCs can accommodate the peak demands for a low discharging current limit on the battery, thereby reducing heat generation. Electrochemical impedance spectroscopy and cyclic voltammetry are performed on SCs to analyze their thermal performance at different temperatures ranging from 0 °C to 75 °C under different bias values of -0.6, 0, 0.6, and 1 V， respectively. The results indicate a higher specific capacitance of the SC at an optimum operation temperature of 25 °C for the studied bias. This study shows that the hybrid combination of the Li-ion battery and SC for a lightweight EV can address peak demands by reducing thermal stress on the Li-ion battery and increasing the driving range.

Abstract:The variability of renewable energy and transmission congestion provide opportunities for arbitrage by merchants in deregulated electricity markets. Merchants strategically invest to maximize their profits. This paper proposes a joint investment framework for renewable energy, transmission lines, and energy storage using the Stackelberg game model. At the upper level, merchants implement investment and operation strategies for deregulated transmission and energy storage to maximize profits. At the middle level, central planners seek to maximize social welfare through investments in centralized renewable energy and energy storage. At the lower level, independent system operators jointly optimize the energy and reserve markets to minimize the total operating costs. Merchants are remunerated through financial rights, which are a settlement method based on locational marginal price. The trilevel optimization problem is reformulated as a tractable single-level one using Karush-Kuhn-Tucker (KKT) conditions and strong duality theory. The interaction between merchants and central planners is studied with an example based on the IEEE 30-bus test system. The assignment of weight coefficients to the corresponding stochastic scenarios can help merchants avoid investment risk, and their effectiveness is verified with the IEEE 118-bus test system.

Abstract:Although wind and solar power is the major reliable renewable energy sources used in power grids, the fluctuation and unpredictability of these renewable energy sources require the use of ancillary services, thereby increasing the integration cost. This study proposes a wind, solar, and pumped-storage cooperative (WSPC) model that can be applied to large-scale systems connected to dispersed renewable energy sources. This model provides an optimized coordinated bidding strategy in the day-ahead market, along with a method to facilitate revenue distribution among participating members. This model takes advantage of the natural complementary characteristics of wind and solar power while using pumped storage to adjust the total output power. In the coordinated bidding strategy, a proportion of the energies is provided as firm power, which can lower the ancillary service requirement. Moreover, a multi-period firm power-providing mode is adopted to reflect the wind-solar output characteristics of each period accurately. The duration of each period is selected as a variable to accommodate seasonal characteristics. This ensures that the provision of firm power can maintain a high proportion under varied connected ratios of wind-solar, thereby obtaining higher revenue. By using the revenue distribution method, the short-term influencing factors of the cooperative model are considered to provide the economic characteristics of wind farms and photovoltaic stations. In this way, revenue distribution can be fairly realized among the participating members. Finally, the effectiveness and economy of the proposed model are validated based on actual data obtained from the power grid in California, USA.

Abstract:This paper analyzes the oligopolistic equilibria of multiple price-maker agents in performance-based regulation (PBR) markets. In these markets, there are price-maker agents representing some frequency regulation (FR) providers and a number of independent price-taker FR providers. A model of equilibrium problem with equilibrium constraints (EPECs) is employed in this paper to study the equilibria of a PBR market in the presence of price-maker agents and price-taker FR providers. Due to the incorporation of the FR providers’ dynamics, the proposed model is reformulated as a mixed-integer linear programming (MILP) problem over innovative mathematical techniques. An optimal equilibrium point is also selected for the market, where none of the agents is the unique deviator and the dynamic performance of power system is improved simultaneously. The effectiveness of the proposed optimal equilibrium point is evaluated by comparing the outputs with the conventional optimal dispatches of the FR providers.

Abstract:This paper investigates the small-signal stability of the hybrid high-voltage direct current (HVDC) transmission system. The system is composed of line commutated converter (LCC) as rectifier and modular multi-level converter (MMC) as inverter under weak AC grid condition. Firstly, the impact of short-circuit ratio (SCR) at inverter side on the system stability is investigated by eigen-analysis, and the key control parameters which have major impact on the dominant mode are identified by the participation factor and sensitivity analysis. Then, considering the quadratic index and damping ratio characteristic, an objective function for evaluating the system stability is developed, and an optimization and configuration method for control parameters is presented by the utilization of Monte Carlo method. The eigenvalue results and the electromagnetic transient (EMT) simulation results show that, with the optimized control parameters, the small-signal stability and the dynamic responses of the hybrid system are greatly improved, and the hybrid system can even operate under weak AC grid condition.

Abstract:The high penetration of distributed generation (DG) has set up a challenge for energy management and consequently for the monitoring and assessment of power quality (PQ). Besides, there are new types of disturbances owing to the uncontrolled connections of non-linear loads. The stochastic behaviour triggers the need for new holistic indicators which also deal with big data of PQ in terms of compression and scalability so as to extract the useful information regarding different network states and the prevailing PQ disturbances for future risk assessment and energy management systems. Permanent and continuous monitoring would guarantee the report to claim for damages and to assess the risk of PQ distortions. In this context, we propose a measurement method that postulates the use of two-dimensional (2D) diagrams based on higher-order statistics (HOSs) and a previous voltage quality index that assesses the voltage supply waveform in a continous monitoring campaign. Being suitable for both PQ and reliability applications, the results conclude that the inclusion of HOS measurements in the industrial metrological reports helps characterize the deviations of the voltage supply waveform, extracting the individual customers’ pattern fingerprint, and compressing the data from both time and spatial aspects. The method allows a continuous and robust performance needed in the SG framework. Consequently, the method can be used by an average consumer as a probabilistic method to assess the risk of PQ deviations in site characterization.

Abstract:This paper investigates a fixed-time distributed voltage and reactive power compensation of islanded microgrids using sliding-mode and multi-agent consensus design. A distributed sliding-mode control protocol is proposed to ensure voltage regulation and reference tracking before the desired preset fixed-time despite the unknown disturbances. Accurate reactive power sharings among distributed generators are maintained. The secondary controller is synthesized without the knowledge of any parameter of the microgrid. It is implemented using a sparse one-way communication network modeled as a directed graph. A comparative simulation study is conducted to highlight the performance of the proposed control strategy in comparison with finite-time and asymptotic control systems with load power variations.

Abstract:An ambient modal framework for inertia estimation using synchrophasor data is proposed in this letter. Specifically, an analytical formulation is developed for the estimation of inertia based on the frequency and damping ratio modes extracted from ambient data. An advantage of the proposed framework is that it can rely on synchronized ambient data under non-disturbed conditions for online estimation and tracking of inertia. Ultimately, numerical simulation studies and physical experiments demonstrate the feasibility of the proposed approach.