Perspective Of System Operator Research Articles

Distributed Generation Allocation in Distribution Network for Energy Losses Reduction

Abstract This paper presents a method for distributed generation (DG) allocation in medium voltage (MV) distribution system based on energy loss minimization. The main objective of the research is to design, implement and test a DG allocation (siting and sizing) method and to investigate how optimal DG allocation influence the operational parameters of the system from the Distribution System Operator (DSO) perspective. The problem is formulated as a single objective optimization problem solved by using both genetic algorithm and particle swarm optimization techniques. Model of a realistic Electric Power Distribution System (EPDS) and IEEE 37-bus EPDS are used as test systems. The results confirm that proposed algorithms can be used for practical DG allocation. The research presented contributes to the field as it provides a DG allocation method for energy loss reduction performed on a EPDS which can be applied in realistic planning and regulatory situations using open-source software.

Optimal AC/DC Distribution Systems Expansion Planning From DSO's Perspective Considering Topological Constraints

As the integration of DC distributed resources increases in recent years, hybrid AC/DC topology emerges as a promising alternative in the planning of distribution systems. This paper presents an AC/DC optimal expansion planning of distribution networks from Distribution System Operator's perspective with a special focus on grid architecture through Integer Programming-based topological constraints. The proposed formulation aims to determine the optimal location and sizing of converters and to select lines type (AC or DC) and state (closed or open). The problem is formulated as a mixed integer second-order conic programming model that guarantees global optimality and verifiable exactness by using off-the-shelf solvers. A multi-scenario approach with a Wasserstein distance-based scenario reduction is applied to deal with resource uncertainty. Case studies based on a 13 bus and IEEE 33 bus distribution networks were used to verify the effectiveness of the model. The results show that a hybrid architecture can improve the efficiency of the grid through optimal active power flow and reduce the total network costs.

A Robust Multiobjective Strategy for Short-Term Distribution System Upgrading to Increase the Distributed Generation Hosting Capacity

This work proposes a strategy that estimates the maximum distributed generation (DG) capacity that can be connected to the electric distribution system (EDS), when EDS upgrading actions are implemented. The problem is viewed from the distribution system operator (DSO) perspective and is formulated as a multiobjective optimization model that maximizes the DG capacity and minimizes the upgrading cost. Upgrading options include allocation of voltage regulators (VRs) and conductor replacement, which are coordinated with the application of generation curtailment, DG reactive power support, and voltage control using VRs. The uncertainty and variability of demand and DG power production are accounted for resorting to a multi-period robust formulation. The problem solution generates a set of upgrading plans, from which the DSO can select the one that meets its economic expectation and provides the EDS with the necessary capacity to accommodate the expected new DG. A post-optimization analysis is proposed to evaluate the impact of EDS upgrading on the DSO profit. Simulations are performed on a 135-bus EDS, and results show that the proposed strategy reveals an upgrading plan that increases the DG capacity up to 64.39% with a gain in the DSO profit of 5.43%, compared to the case without EDS upgrading.

Investigation of membrane wetting for CO2 capture by gas–liquid contactor based on ceramic membrane

In recent years, global climate change caused by excessive CO2 emissions has attracted wide attention. Within this context, CO2 capture by gas–liquid contactor based on membranes is one of the most promising technologies for post-combustion CO2 capture. However, mass transfer deterioration due to membrane wetting becomes one of barriers for its development. In this paper, based on Laplace-Young equation, reliable individual membrane pore wetting model is constructed to demonstrate detailed wetting process inside membrane pores, which is verified by molecular dynamic (MD) numerical simulation. The wetting conditions of ceramic membrane with 1.26 µm average pore size and 51.4° contact angle are investigated in experiments. The results show that neither average nor maximum pore diameters can accurately predict liquid and bubble breakthrough pressures of ceramic membrane. The actual liquid and bubble breakthrough pressures of ceramic membrane are slightly lower than critical liquid and bubble breakthrough pressures corresponding to the maximum pore size. The wetting state of ceramic membrane can be determined by combining individual pore wetting model and actual membrane pore distribution. In addition, for practical application process of CO2 capture by gas–liquid contactor with ceramic membrane, some suggestions to avoid membrane wetting are proposed for selection of ceramic membrane and system operation perspective. The membrane wetting analysis method proposed in this paper not only contributes to future applications of ceramic membrane in CO2 capture, but is also widely applicable to other gas–liquid contactors in membrane separation process.

The roles of carbon capture, utilization and storage in the transition to a low-carbon energy system using a stochastic optimal scheduling approach

Decarbonizing energy sector through deploying the technology of carbon capture, utilization and storage (CCUS) is required to keep global temperature rise below 1.5 °C by 2060. The true roles of CCUS have to be validated from the perspective of integrated energy system with features of high penetration of uncertain renewable power and strong interactions among multiple energy generation, conversion and storage technologies. To this end, this paper proposes a stochastic power-heat-gas-carbon scheduling approach for the wind-solar-thermal-battery energy mix integrated with CCUS (WSTB-CCUS) to manage the operation of energy conversion devices and CCUS in a coordinated manner. Coal/biomass co-fired and natural gas fired cogeneration units integrated with carbon capture system are built to reflect complex relationship between fuel, power, heat and carbon. A stochastic optimization model based on chance-constrained programming is presented to fully consider the uncertainties of renewable power and load demands. Case study shows that the stochastic scheduling can reduce 7.25% of average total operating costs compared with the deterministic scheduling by providing a more robust solution for the WSTB-CCUS. Discussions are then carried out to explore the roles of CCUS in carbon reduction, flexibility enhancement, negative emission and robustness improvement from the perspective of energy system operation through stochastic scheduling. The influences of other low carbon technologies on the operation of carbon capture are further discussed to provide more insight on the low carbon transformation of energy system.

A methodological approach for assessing the value of energy storage in the power system operation by mid-term simulation

This paper presents a novel methodological approach to assess the operational value of energy storage by mid-term simulation considering the co-optimization of energy and reserves provision in the day-ahead electricity market (DAM) framework. A 0/1 mixed integer linear formulation of the traditional Unit Commitment (UC) problem from the Transmission System Operator (TSO) perspective is utilized, allowing for the realistic and accurate modeling of both conventional and storage units’ operating phases and conditions. Multiple thermal units’ availability scenarios are generated and combined with different climatic conditions (load demand, RES and hydro generation) to formulate a large number of simulation scenarios. Parallel High-Performance Computing (HPC) infrastructure has been employed for the efficient execution of the computationally intensive simulation runs. The proposed optimization model has been tested in the future Greek mainland power system (year 2025) in accordance with the provisions of the approved Greek National Energy and Climate Plan and the most recent Greek TSO Adequacy Study. Simulation results illustrate that the addition of up to 780 MW of new closed-loop energy storage units can lead to higher DAM clearing prices (up to +2.1%), lower conventional thermal units cycling (up to -39.7%) and lower RES curtailment volumes (up to -93.5%) for the base case scenarios. Additionally, the increasing closed-loop energy storage capacity up to 780 MW affects negatively its system and market value, which decrease from 27.4 k€/MW to 16.0 k€/MW (system value) and from 16.5 k€/MW to 4.4 k€/MW (market value), respectively. Sensitivity analysis on the forecasted RES penetration level demonstrated that the operation of up to 3,000 MW of additional wind and PV plants can increase energy storage system value by 86–136% and market value by 184–320% with respect to the base case, thus indicating that the higher the RES penetration is, the higher the value of energy storage.

Ex-post evaluation of Interruptible Load programs with a system optimisation perspective

The deployment of demand response in reserve markets has been widely discussed. Interruptible Load programs contract demand capacity from consumers in exchange for fixed and variable payments. System operators contract these resources to increase system resilience and use them for economic purposes. Although common, no ex-post evaluations of the program’s performance and efficiency exists in the literature. To fill this gap, the paper presents a procedure to evaluate the optimal usage of these resources from a system operator perspective. A Mixed Integer Linear Problem is set to minimise the overall cost of the system using the participant demand resources in the tertiary reserve market, while ensuring that all technical and regulatory constraints are fulfilled in the evaluation. The proposed method describes a series of metrics to compare the optimal performance with the current scenario, and we draw a set of conclusions and policy recommendations from it. We apply the method to the Spanish Interruptible Load program. Our results show that during the five and a half years of the program, demand resources could have provided savings to the system of up to 23% of the cost of tertiary reserve.

Electric water heater flexibility potential and activation impact in system operator perspective – Norwegian scenario case study

Simultaneous activation of demand side flexibility on thermostatically controlled loads will cause adverse impacts later in the form of rebounds. Distribution system operators must know the characterisation of the impact, as they are responsible for voltage quality of power delivery and suffer the loss of lifespan of network components due to overloading. In this paper, characterising parameters for flexibility activation on electric water heaters (EWHs) are proposed and flexibility potentials are computed considering smart activation methods for the Norwegian scenario. The proposed parameters are rebound percentage, delay, ramp rates, second peak distance, activation error, flexible power, and temperature deviation. Four scenarios with different levels of flexible power and activation time are developed in the Norwegian context for quantification of the flexibility potentials and the parameters. The highest average flexible power potential is 53.9% at 8:00 a.m. for a duration of 61 min. EWHs flexibility activation can serve as Frequency Containment Reserves (FCR) at peak demand hours with high ramp-up and ramp-down rates of 48.5% and 23.8% per minute and as Frequency Restoration Reserves (FRR) during non-peak hours.

The influence of open-phase operation of inverter side AC subsystem on commutation process and countermeasures

Commutation failure can lead to DC power fluctuation or even DC pole blocking, threatening the safety and stability of power system. As an abnormal operation state, open-phase operation of inverter side AC subsystem (OPOISAS) occurs at times and affects commutation process. In this paper, the influence of OPOISAS on commutation process is analyzed first. And the analysis shows that the larger the negative-sequence voltage amplitude (NSVA) of the commutation bus is, the greater the risk of commutation failure will be. To deal with the risk of commutation failure, the formula of the negative-sequence voltage of commutation bus during OPOISAS is deduced. Based on the formula, the law of NSVA variation with different factors is analyzed. Meanwhile, several countermeasures are given from the perspective of system planning and operation to reduce the risk of commutation failure during OPOISAS. Simulation results demonstrate that the analysis is correct and the countermeasures are effective.

Blockchain-Based Energy Applications: The DSO Perspective

This paper discusses blockchain-based energy applications from the distribution system operator (DSO) perspective. Blockchain has a potential impact on newly emergent actors, such as electric vehicles (EVs) and the charging facility units (CFUs) of the electricity grid. Although Blockchain offers magnificent decentralized solutions, the central management of DSOs still plays a significant, non-negligible role, owing to the reality of the existing grid structure. Numerous related studies of proposed blockchain-based EV systems have investigated the energy costs of EVs, fast and efficient charging, privacy and security, P2P energy trading, sharing economy, the selection of appropriate CFUs location, and scheduling. However, cooperation with DSO organizations has not been adequately addressed. Blockchain-based solutions mainly suggest an entirely distributed and decentralized approach for energy trading; however, converting the entire power system infrastructure is considerably expensive. Building a thoroughly decentralized electricity network in a short time is nearly impossible, particularly at the national grid level. In this regard, the applicability of the solutions is as significant as their appropriateness, especially from the DSO perspective, and must be examined closely. We searched and analyzed the blockchain literature related to EVs, CFUs, DERs, microgrids, marketing, and DSOs to define the DSO-based requirements for potential blockchain applications in the energy sector, specifically EV evolution.

Optimal Location of Distributed Energy Resources Considering Investment Costs, Use of Resources and Network Constraints

The consideration of distributed energy resources as non-wires alternatives for the elimination of overloads and voltage problems poses new challenges for their location and management. Traditional location methodologies approach the problem from the distribution system operator perspective, who tries to correct overload and voltage outside margins while minimizing other possible negative impacts in the network. The approach presented in this paper also considers the investor’s point of view, seeking the most appropriate technology alternative, the maximum use of the available resource, and the minimum investment. Those aspects are explicitly taken into account in the formulation of the model, which takes the form of a mixed-integer nonlinear optimization problem. The proposed methodology is tested in two different scenarios using the medium-voltage distribution system benchmark of CIGRE. The results show that the interests of the distribution system operator and the investors can be jointly satisfied, achieving an effective, safe and realistic integration of new distributed resources.

The role of energy storage and demand response as energy democracy policies in the energy productivity of hybrid hub system considering social inconvenience cost

Energy liberation policies are very effective in the development of energy democracy in society. The Demand Response Programs (DRPs) and Electrical Energy Storages (EESs) are the core activities of energy democracy in the energy and social science policies. Furthermore, energy democracy programs have an influential role in the energy productivity. Moreover, the energy productivity concept divides into energy effectiveness and energy efficiency programs. Therefore, the contribution of EESs and DRPs in the effectiveness aspect of energy productivity is significant. However, the implementation of DRPs from the system operator perspective in the smart society causes to increase the social inconvenience of customers. Therefore, this study provides a novel multi-objective probabilistic economic and environmental energy management structure to evaluate the role of EESs and social inconvenience of DRPs on the energy hub system incorporating various carriers such as electrical, heat, gas, and water infrastructure. The energy interdependency causes to increase the scheduling flexibility in the energy hub system. Furthermore, the seawater desalination units make the significant potential of water management in the remote energy hub areas that are near to the seawater. Therefore, the proposed model considers the role of programs and technologies (such as EESs and DRPs) in the optimal power and water nexus problem. Also, the proposed multi-objective function is modeled as a MILP function and the problem is solved using the CPLEX solver in the GAMS software. The results show that the Sensitivity Coefficient of Customers (SCCs) in DRPs has significant effects on the effectiveness of DRPs and EESs in the energy hub system. Indeed, considering social energy democracy policies increases the energy productivity of the hub system.

Optimal Operational Scheduling of Distribution Network with Microgrid via Bi-Level Optimization Model with Energy Band

An optimal operation of new distributed energy resources can significantly advance the performance of power systems, including distribution network (DN). However, increased penetration of renewable energy may negatively affect the system performance under certain conditions. From a system operator perspective, the tie-line control strategy may aid in overcoming various problems regarding increased renewable penetration. We propose a bi-level optimization model incorporating an energy band operation scheme to ensure cooperation between DN and microgrid (MG). The bi-level formulation for the cooperation problem consists of the cost minimization of the DN and profit maximization of the MG. The goal of the upper-level is to minimize the operating costs of the DN by accounting for feedback information, including the operating costs of the MG and energy band. The lower-level aims to maximize the MG profit, simultaneously satisfying the reliability and economic targets imposed in the scheduling requirements by the DN system operator. The bi-level optimization model is solved using an advanced method based on the modified non-dominated sorting genetic algorithm II. Based on simulation results using a typical MG and an actual power system, we demonstrate the applicability, effectiveness, and validity of the proposed bi-level optimization model.

Distributed Routing and Charging Scheduling Optimization for Internet of Electric Vehicles

In this paper, we consider an Internet of Electric Vehicles (IoEV) powered by heterogeneous charging facilities in the transportation network. In particular, we take into account the state-of-the-art vehicle-to-grid (V2G) charging and renewable power generation technologies implemented in the charging stations, such that the charging stations differ from each other in their energy capacities, electricity prices, and service types (i.e., with or without V2G capability). In this case, each electric vehicle (EV) user needs to decide which path to take (i.e., the routing problem) and where and how much to charge/discharge its battery at the charging stations in the chosen path (i.e., the charging scheduling problem) such that its journey can be accomplished with the minimum monetary cost and time delay. From the system operator's perspective, we formulate a joint routing and charging scheduling optimization problem for an IoEV network, and show that the problem is NP-hard in general. To tackle the NP-hardness, we propose an approximate algorithm that can achieve affordable computational complexity in large-size IoEV networks. The proposed algorithm allows the routing and charging solution to be calculated in a distributed manner by the system operator and EV users, which can effectively reduce the computational complexity at the system operator and protect the EV users' privacy and autonomy. Besides, a proximal method is introduced to improve the convergence rate of the proposed algorithm. Extensive simulations using real world data show that the proposed distributed algorithm can achieve near-optimal performance with relatively low computational complexity in different system set-ups.

Integration of renewable energy resources from the perspective of the Midcontinent Independent System Operator: A review

Due to the abundance of wind resources in its footprint and favorable energy policies and economics, the Midcontinent Independent System Operator has seen an increase of wind energy to its fuel mix since 2006. With over 11,000 MW under its control and 7000 MW on the generation interconnection queue, MISO has begun to act with respect to understanding what higher levels of variable renewable energy resources would mean for its system.

A Framework to Split the Benefits of DR Between Wind Integration and Network Management

In power systems today, considerable developments are being made in the energy transition from centralized fossil fuels to renewable distributed generation (DG) sources. However, this ongoing progress has presented several challenges to the operation and planning of distribution systems due to the variability of intermittent renewable generation. Demand response (DR) is widely regarded as a feasible tool to provide a seamless transition by altering the load profiles according to the intermittent generation profile. However, the resultant volatile power flows can be taxing for network capacity. This paper offers a framework from the distribution system operator's perspective for the optimal utilization of DR between DG curtailment mitigation and network management. The application of the developed framework to a generic Finnish distribution system demonstrates that the benefits of DR should be envisioned for network management as long as the wind curtailment rate is below a certain level. This means that, beyond the threshold energy curtailment rate, the distribution system operator would be more economically efficient by making network reinforcements.

Investigation of Narrow-Band Power-Line Carrier Communication System Performance in Rural Distribution Grids

This paper investigates error performance of the narrow-band (NB) power-line carrier (PLC) communication system from a distribution system operator (DSO) perspective, with measurement data collected in the rural 400 V distribution grid. The performance evaluation is founded on the three aspects: attenuation of the NB PLC channel, frequency of the signal to noise ratio (SNR) class occurrence detected in a receiver and bit error rate (BER) analysis. The true BER is estimated from the limited amount of collected data using error model based on Neyman type A contagious distribution, appropriate for communication channels with impulsive noise. Results confirmed that PRIME-based NB PLC deployments for smart metering applications are adequate in rural distribution grids, even in cases with high attenuation and articulated frequency variations in a PLC channel. DOI: http://dx.doi.org/10.5755/j01.eie.24.1.20149

What does a distribution system operator need to know about the findings of the future power system architecture project?

The Future Power System Architecture (FPSA) project examines the changes to the GB power system expected over the next 15–20 years, and the challenges these will present. The project brief considered functions from a whole system perspective. This study summarises the key findings and conclusions from the distribution system operator (DSO) perspective.

Energy Management Strategy for Plug-In Hybrid Electric Vehicles via Bidirectional Vehicle-to-Grid

As a paradigm of the incoming smart grid, vehicle-to-grid (V2G) has been proposed as a promising solution to increase the adoption rate of plug-in hybrid electric vehicles (PHEVs). In this paper, we investigate the energy management strategies for PHEVs via bidirectional V2G. We first follow a cost-conscious approach from the PHEV owner point of view. To minimize the daily energy cost, we formulate the energy management problem via dynamic programming (DP). However, the “well-known” complexity in solving DP poses a computational challenge even for a small number of iterations. Therefore, we propose a state-independent four-threshold $(s,S,s^{\prime},S^{\prime})$ battery charging/discharging policy and theoretically prove the optimality of the proposed energy management strategy based on stochastic inventory theory. A backward iteration algorithm is further developed to practically implement the $(s,S,s^{\prime},S^{\prime})$ feedback policy. Second, from the distribution system operator's perspective, we aim to shave the peak load and flatten the overall load profile. To this end, we propose an optimal PHEV charging scheme and further derive a reminiscent “water-filling” solution for this scenario. Realistic PHEV battery models, time-of-use electricity pricing rate, and real data of household demand are integrated into our formulated V2G system model. The theoretical analysis and proofs are instrumental to the future large-scale PHEV adoption in smart grids.

Bi‐level modelling of false data injection attacks on security constrained optimal power flow

Conventional power system was originally designed to provide efficient and reliable power. With the integration of information technology and advanced metering infrastructure, the power grid has become smart. The smart meters have allowed the system operators to continuously monitor the power system in real time and take necessary action to avoid system failures. Malicious actor, with access to the smart meters can modify sensor measurements to disrupt the operation of power system. To make the power system resilient to such cyber-attacks, it is important to study all possible outcomes of cyber-intrusions. In this paper, we present an attack on security constrained optimum power flow. We show with the help of case studies how an attacker, by injecting false data in load measurement sensors, can force system operator to change the dispatch and hence make the power system N–1 in-compliant. The attack is modeled as a bi-level optimization problem, aiming to find the minimum set of sensors required to launch the attack. From the system operator's perspective, critical lines and critical generators vulnerable to false data injection (FDI) attack are identified. IEEE 14 bus and 30 bus test systems are used to test the vulnerability of the power system against FDI attacks.