Showing papers by "Matti Lehtonen published in 2023"

Optimum estimation of series capacitors for enhancing distribution system performance via an improved hybrid optimization algorithm

Abstract: As the load on distribution networks grows, system operators and planners are constantly challenged with the issue of voltage regulation or enhancing the quality of supply to customers at the load end of lengthy distribution lines. This paper presents the optimum determination of series capacitor units in a distribution system to maximize energy‐saving and enhance voltage levels. Interestingly, series capacitors can enhance the capability of transmission lines, reduce line losses, enhance the performance of buses with large induction motor loads and reduce voltage flicker. At the same time, the limitations of series compensation are taken into consideration while calculating its optimum values. To achieve the planning objective and optimal load flow objective, two strategies: The Improved Grey Wolf Optimization method (I‐GWO) and Tabu Search (TS), are hybridized to get the benefit of their advantages. The I‐GWO has a movement strategy called dimension learning‐based hunting for enhancing the balance between global and local search and maintaining diversity. The proposed (I‐GWO‐TS) algorithm can solve mixed‐integer programming to achieve the planning and the optimal load flow objectives. The proposed method can be applied to a real Egyptian distribution system that is heavily loaded, with poor voltage regulation, and also has high‐power losses. The obtained results demonstrate the capability of the proposed approach to determine optimal series capacitors’ location and sizing for maximization of energy saving. Further, the proposed method improves the network performance regarding the voltage profile and power losses, although the limitations of including series compensation were considered in the distribution system.

Cost-Optimal Renovation Solutions for Detached Rural Houses in Severe Cold Regions of China

Abstract: High heating expenses are observed in numerous Chinese rural houses located in severe cold regions due to the high heating demand, inferior envelope performance and low-efficiency heating equipment. The local traditional heating methods include Chinese Kangs and coal boilers with water-based radiators. The intermittent operation and manual regulation of these systems result in significant temperature differences and inadequate thermal comfort. This study presents the cost-optimal envelope renovation solutions with the minimized lifecycle cost (LCC) during a 20-year discount period and CO2 emissions of annual delivered energy consumptions. A single-family detached rural house in Harbin was used as a case building, illustrating the typical state of comparable houses in this climate context. Simulation-based multi-optimization analysis was conducted in this study using the building simulation tool IDA ICE and its integrated optimization tool AutoMOO. The results indicate that the cost-optimal renovation solutions with intermittent and continuous heating can cut CO2 emissions by 30% and 40%, respectively. The LCC with intermittent heating is still 7% greater than its pre-renovation case, which may require external financial support to encourage the renovation conduction, while the LCC with continuous heating decreased by 8% after renovation. According to the comparison results, cost-optimal solutions have significant advantages in both reductions of LCC and CO2 emissions over standard-based solutions. Moreover, utilizing intermittent heating is more effective than continuous heating in demonstrating the positive impacts of envelope renovation on increasing average temperature, decreasing temperature differences and lowering occupied time at low thermal comfort levels.

Hosting capacity in distribution grids: A review of definitions, performance indices, determination methodologies, and enhancement techniques

Abstract: For the past few years, the world has seen a great shift toward renewable energy resources from conventional ones. But the ever‐increasing integration of distributed generation (DG) to the electrical network leads to integration limiting constraints like overvoltage, under voltage, harmonics, equipment ampacity violations, and failure of protection schemes. Therefore, an extensive investigation of the methodologies in which DGs can be incorporated into the electrical network is presented in this manuscript. This article provides an extensive review of all the hosting capacity (HC) terms, references, limiting constraints of the studied networks, geographical segregation, and their determination methodologies. Moreover, the factors defining the HCs of various networks and the architectures employed to increase them, are also explained briefly in the conducted review study.

A Distributed Framework for Intense Ramping Management in Distribution Networks

Abstract: High-penetration of renewable energy sources (RESs) in power networks has resulted in new operational challenges in the system. Accordingly, due to the uncertainty as well as variability of power-outputs of RESs, the flexibility ramping capacity of the system should be improved. Accordingly, system operators would rely on local responsive resources (LRSs) in distribution networks (DNs) to guarantee the demand-supply balance in each area of the system and minimize its associated ramping requirements. Nevertheless, the introduction of multi-microgrid (multi-MG) structures would limit the direct-access of system operators over the LRSs scheduling. As a result, this paper aims to develop a novel framework for intense-ramping management in multi-MG systems to settle the demand-supply gap in the system while addressing the distributed nature of the network. Respectively, alternating direction method of multipliers (ADMM) is employed to develop a decentralized coordination scheme in the system. Moreover, transactive energy control signals are utilized in the context of the ADMM-algorithm in order to exploit the LRSs scheduling to address the ramping constraints of the overall system. Lastly, the scheme is simulated on 37-bus and 123-bus DNs to analyze its efficacy in the management of intense ramping conditions in multi-MG DNs.

Multi-Objective Allocation of EV Charging Stations and RESs in Distribution Systems Considering Advanced Control Schemes

Abstract: Renewable energy sources (RESs) and electric vehicle charging stations (EVCSs) have been extensively incorporated into distribution systems. Due to the stochastic nature of RES generation and electric vehicles (EVs), risky operational challenges might impact the grid. Therefore, this paper proposes a multi-objective planning framework to optimally allocate EVCSs in conjunction with RESs. Specifically, the proposed RESs and EVCSs planning framework considers three sub-objectives to be minimized, i.e., voltage deviations, energy losses, and EVs owners’dissatisfaction. Further, active power curtailment of RESs is precluded while considering the diverse operational constraints of the grid, RESs, and EVCSs. To provide further benefits, advanced control schemes of the interconnecting RES inverters, grid-to-vehicle (G2V), and vehicle-to-grid (V2G) schemes are considered in the proposed framework. A two-level approach is developed to solve this holistic framework with competing sub-functions to obtain the Pareto-optimal solutions. The outer optimization level precisely optimizes the RES locations and sizes, along with the optimal places of the EVCS. On the other hand, the inner level determines the optimal EVCS charging considering stochastic EV power, RES inverter reactive power, and the time-of-use energy tariff. The proposed framework has been tested on the 69-bus distribution system. The simulation results reveal the efficacy of the proposed RES and EVCS planning framework. The total voltage deviation and energy losses achieved by the proposed framework are reduced by 96% and 74%, respectively, with respect to the uncontrolled charging of EVs, while the owners’ satisfactions are met.

Exploiting DERs’ Flexibility Provision in Distribution and Transmission Systems Interface

Abstract: This paper presents a new market-based framework to exploit distributed energy resources’ (DERs) flexibility in distribution and transmission systems. This framework targets intra-hourly flexibility requirements of energy systems. In this regard, a local market is operated in distribution network to exploit DERs’ capabilities. Local market is cleared in coordination with wholesale market to optimize DERs’ flexibility procurement for meeting intra-hourly variability and uncertainty in distribution and transmission systems. In the proposed framework, a technical virtual power plant (TVPP) operates day-ahead (DA) local energy and flexibility markets in which DER aggregators take part. Furthermore, the TVPP participates in DA wholesale energy and flexibility markets which are cleared by the market operator and the transmission system operator, respectively. In this framework, all agents’ preferences and their transactions are addressed using a bilevel model with multiple lower levels. The proposed model is reformulated into a mixed integer linear programming problem by utilizing strong duality theory, Karush-Kuhn-Tucker optimality conditions and primal-dual counterpart. The model is implemented on an IEEE standard test system. The results demonstrate effectiveness of the framework in utilizing DERs’ capabilities to meet flexibility requirements as well as satisfying agents’ preferences.

Vulnerability of EV Charging Stations to Cyber Attacks Manipulating Prices

Abstract: Electric vehicles (EVs) have experienced an unprecedented increase in their penetration; in well developed countries; due to the advancement of battery technology and the need to make transportation more environmentally friendly. As a result of this trend, EVs have become an integral part of those countries power grid ecosystem, where they may be used as both a source, and a consumer of energy. However, cybersecurity risks associated with large fleets of EVs within the power grids can significantly impact the normal operation of distribution systems. This paper considers the cyber-attack consequences, from attackers’ perspective. The cyber attackers under study target the charging price (CP) by increasing the CP in off-peak hours and accordingly subsiding it in on-peak hours. A two-stage optimization framework is proposed to identify the most important ultra-fast charging stations (XFCs) under the cyber-attack, meanwhile, the EV users’ response to the compromised CP is considered. Then, the proposed model is implemented on a modified IEEE 123-bus distribution test system, and the effectiveness of the model is proved through some case studies. In particular, they demonstrated that the amount of load shedding is increased by 8.198 MW when the EV user response is taken into account.

On PV Hosting Capacity of Distribution Networks

Abstract: This paper provides a summary of the research regarding photovoltaic hosting capacity of the distribution networks in Finnish circumstances. Characteristic to these circumstances is high and diverse seasonal variations of electricity demand and photovoltaic generation. The paper presents key findings of the hosting capacity and discusses different methods and approaches when analyzing the hosting capacity questions both from the distribution systems operator and the prosumer point of view.

Novel Industry Architectures for Connectivity Solutions in the Smart Distribution Grids

Abstract: The electric energy system is undergoing a major change due to the increasing requirements of dynamic performance. In distribution grids, this evolution will necessitate expanded automation, which in turn will require enhanced connectivity solutions. Strongly evolving communications technologies and architectures, particularly mobile communications as well as cloud and edge computing, will provide new opportunities and alternatives for connectivity solutions. This paper contributes by identifying potential technical and industry architectures for the connectivity solutions required to manage distribution grids in the early 2030s. The study utilizes a senior expert panel and a Delphi survey. Industry architectures are modelled as value networks. The paper uses the Finnish distribution grids as a case example. Regarding technical architectures, the results reveal skepticism concerning those emerging 5G mobile network features that target industrial applications and about the need for extensive distributed computing in the proximity of consumers and prosumers. The most probable industry architectures are found to be those that enable the Distribution System Operators (DSOs) to maintain direct control of critical technical components, or that enable Communications Service Providers (CSPs) to handle the operations of both communications solutions and distributed computing. CSPs are seen as well positioned for this task due to their existing networking and computing infrastructure. However, this may also involve business risks for both DSOs and CSPs.

Hedging Investments of Grid-Connected PV-BESS in Buildings Using Cryptocurrency Mining: A Case Study in Finland

Abstract: In recent decades, there has been a growing global focus on solar power as a renewable energy source (RES) to supply local energy demands and reduce greenhouse gas emissions. Rooftop solar photovoltaic (PV) system provides a small-scale utilization of solar energy on the roofs of apartment buildings. Investment in this system and its profitability depends on several factors, including geographic conditions, electricity price, and local load profiles. However, in Finland, the maritime and continental climates and electrically heated residential buildings present unique challenges to the investment and utilization of rooftop PV systems. Common solutions to incentivize the investment of grid-connected PV in apartments are battery energy storage systems (BESSs), demand side management (DSM), and power-to-x (P2X) approaches. Nevertheless, the value of these solutions is limited in Finland due to the seasonal variation of solar PV generation and customers’ energy consumption. This paper presents a novel and practical control and hedging mechanism to encourage investments in rooftop solar PV-BESS systems by investing in cryptocurrency mining devices (CMDs) as dispatchable and flexible loads, which facilitate the use of excess renewable energy for producing cryptocurrency, such as bitcoin (BTC). This mechanism can optimally switch the output of excessive renewable energy between exporting to the main grid and mining cryptocurrency. The proposed mechanism is studied using a dataset obtained from a residential apartment building in Helsinki, Finland, and its effectiveness is demonstrated through several practical scenarios. The results of a case study employed in this work demonstrate that the proposed hedging mechanism can provide sufficient encouragement for investors to invest in a PV system, with a return on investment equal to 57.7%. This mechanism also reduces the annual cost of residential apartments by 68.1%.

Studying Direct Lightning Stroke Impact on Human Safety Near HVTL Towers Considering Two Layer Soils and Ionization Influence

Abstract: A lightning strike is considered one of the most risky natural phenomena that can lead to human harmful and the surrounding soil layers. To tackle this issue, this article investigates the influence of direct lightning characteristics in terms of human body safety. Specifically, such investigation is carried out on the effect of resistivities of two-layer soils on human safety when lightning stroke hits the towers of the high voltage transmission lines (HVTLs). The merit of the proposed study is that the soil ionization phenomenon is taken into consideration. Further, the study focuses on the current passing through the human heart, when step and touch (contact) voltages are generated by grounding potential rise, caused by direct lightning strikes transmission tower and the produced potential rise that a person could be exposed. Also, studying the effects of peak current and time of lightning strokes are investigated. Additionally, the paper presents the effect of different reflection factors on human safety. For validation purposes, the ATP program is used in the simulation of the grounding system as well as the human body model. Numerous simulations were accomplished in order to examine the behavior of the current passing through with the human heart. Based on the simulation results, it was concluded that the soil characteristics have superior influences on the contact and step potentials and, accordingly, the survival threshold.

Coordinating Demand Response and Wind Turbine Controls for Alleviating the First and Second Frequency Dips in Wind-Integrated Power Grids

Abstract: This paper introduces a method employing demand response (DR) and wind turbine (WT) to decrease the first and second frequency dips in power systems with the massive proliferation of wind resources. Inverter air conditioners (IACs) are utilized to participate in the DR program. The buildings' thermal model and the IACs' electrical model are integrated to characterize a relation between power system frequency and IACs regulation power. An adaptive delay controller (ADC) is presented to capture the command communication delays from the DR controller to IACs. In addition, a controller is suggested to adopt wind power plants in system frequency regulation. This controller rises the wind plant output power for some seconds and returns it, following frequency recovery, to the initial power. The recovery period causes the second frequency dip, in response to which, a method is introduced to identify the moment of the second dip and alleviate it via DR.

Towards energy‐efficient smart homes via precise nonintrusive load disaggregation based on hybrid ANN–PSO

Abstract: Nowadays, the load monitoring system is an important element in smart buildings to reduce energy consumption. Nonintrusive load monitoring (NILM) is utilized to determine the power consumption of each appliance in smart homes. The main problem of NILM is how to separate each appliance's power from the signal of aggregated consumption. In this regard, this paper presents a combination between particle swarm optimization (PSO) and artificial neural networks (ANNs) to identify electrical appliances for demand‐side management. ANN is applied in NILM as a load identification task, and PSO is used to train the ANN algorithm. This combination enhances the NILM technique's accuracy, which is further verified by experiments on different datasets like Reference Energy Disaggregation Dataset, UK Domestic Appliance‐Level ElectricityUK‐DALE, and Indian data for Ambient Water and electricity Sensing. The high accuracy of the proposed algorithm is verified by comparisons with state of the art methods. Compared with other approaches, the total mean absolute error has decreased from 39.3566 to 18.607. Also, the normalized root mean square error (NRMSE) method has been used to compare the measured and predicted results. The NRMSE is in the range of 1.719%–16.514%, which means perfect consistency. This demonstrates the effectiveness of the proposed approach for home energy management. Furthermore, customer behavior has been studied, considering energy costs during day hours.

Day-Ahead Optimal Scheduling of Resources in a Distribution System in Joint Energy, Regulation and Ramp Markets

Abstract: The increasing trend of integrating Renewable Energy Sources (RESs) into power systems could cause operational challenges in the system from flexibility and reliability perspectives. Respectively, utilities would be relied on local flexible resources to address the flexibility requirements in power systems. In this respect, local resources in distribution systems could participate in ancillary markets along with the wholesale energy markets to address the flexibility capacity shortage in the system. Consequently, this paper aims to provide a framework to optimize the scheduling of resources in distribution networks while participating in energy, regulation, and ramp markets. Correspondingly, this approach would maximize the profits of the local resources in distribution systems whereas providing Flexible Ramp Product (FRP) and regulation reserve for the system operators. Respectively, an optimized bidding strategy is developed to maximize the profits of the local resources, while modeling the operational constraints of the distribution grids and power losses to improve the accuracy of the proposed scheme. Furthermore, the chance constrained concept is taken into account in the proposed scheme to model the uncertainty of RESs. Finally, the model is applied on the IEEE-37-bus-test-system and the sensitivity analysis is employed to investigate its effectiveness in the management of the system.

Developing an Optimal Framework for PMU Placement Based on Active Distribution System State Estimation Considering Cost-Worth Analysis

Abstract: Due to low levels of observability and automation in active distribution networks (ADNs), the deployment of accurate measurement devices would be inevitable to increase the network observability. This work develops an optimal framework for phasor measurement unit (PMU) placement considering the accuracy of the distribution system state estimation (DSSE) process. In this regard, first, considering the significance of active power in supplying network loads, an active power sensitivity analysis is conducted in which the accuracy of active power injection at each bus is multiplied by its value of lost load. In this way, the accuracy of active power estimation could be transformed into a monetary value. Then, based on the determined sensitivity criterion, the optimal placement of PMUs has been performed with the aim of appropriate accuracy for the DSSE. On the other hand, the accuracy of the results obtained in the state estimation procedure can affect the estimation of line-loadings and lead to load shedding due to the low accuracy of estimated line-loadings. It is noteworthy that due to the high integration of distributed energy resources in distribution systems, ADNs would be prone to congestion issues. Therefore, in the next stage, this perspective is also used for determining the optimal number and location of PMUs in ADNs to improve their observability based on the operational conditions of the network. Finally, the developed algorithm is applied on the 77-bus-UK-test distribution network to investigate its effectiveness in improving the DSSE accuracy and mitigating interrupted loads. The numerical results indicate that implementing the proposed framework not only increases the DSSE accuracy but also decreases the cost of compensating the accuracy of active power by 7% compared to the typical network without PMUs. Further, the results show that the proposed model significantly mitigates the total curtailed loads due to the low accuracy of estimated line-loadings.

Influence of Mineral Oil-Based Nanofluids on the Temperature Distribution and Generated Heat Energy Inside Minimum Oil Circuit Breaker in Making Process

Abstract: The enhancement of the thermal properties of insulating oils has positively reflected on the performance of the electrical equipment that contains these oils. Nanomaterial science plays an influential role in enhancing the different properties of liquids, especially insulating oils. Although a minimum oil circuit breaker (MOCB) is one of the oldest circuit breakers in the electrical network, improving the insulating oil properties develops its performance to overcome some of its troubles. In this paper, 66 kV MOCB is modeled by COMSOL Multiphysics software. The internal temperature and the internally generated heat energy inside the MOCB during the making process of its contacts are simulated at different positions of the movable contact. This simulation is introduced for different modified insulating oils (mineral oil and synthetic ester oil) with different types of nanoparticles at different concentrations (0.0, 0.0025, 0.005, and 0.01 wt%). From the obtained results, it is noticed that the thermal stress on the MOCB can be reduced by the use of high thermal conductivity insulating oils. Nano/insulating oils decrease internal temperature and generate heat energy inside the MOCB by about 17.5%. The corresponding physical mechanisms are clarified considering the thermophoresis effect.

Dynamic Model of a Virtual Air Gap Reactor

Abstract: Variable reactors have been a vital component of power networks for decades, where they have been used as fault-current limiting devices or for reactive power compensation. Traditionally, modifying the inductance of predominantly mechanically operated variable reactors requires seconds to minutes. In contrast, virtual air gap (VAG) reactors can change the inductance within milliseconds, potentially improving power system stability. Existing dynamic models of VAG reactors cannot capture the entire system dynamics, limiting their applicability for simulations in the time-domain. This research presents two dynamic VAG reactor models, one with and one without core losses. The models capture all significant system dynamics using electromagnetic principles and VAG reactor flux linkage behavior. The proposed models were experimentally validated using a small VAG reactor. Over a broad operating range, both models accurately reproduce the dynamic behavior, transient response, and dominant harmonics of the small VAG reactor. Consequently, the models may be used for a variety of applications, such as time-domain simulations, harmonic analysis, and the development of suitable controllers for VAG reactors. In addition, engineers may use the core loss omitting model as a VAG reactor design tool, as the actual reactor is not required for modeling.

On the MILP Modeling of Remote-Controlled Switch and Field Circuit Breaker Malfunctions in Distribution System Switch Placement

Abstract: Installing sectionalizing switches and field circuit breakers (FCBs) is vital for the fast restoration of customer electricity supply in distribution systems. However, the high capital costs of these protection devices, especially remote-controlled switches (RCSs) and FCBs, necessitate finding a trade-off between their costs and financial benefits. In this study, we propose a mixed-integer linear programming (MILP) model for optimizing switch planning in distribution systems. The proposed model determines the optimal allocation of manual switches, RCSs, and FCBs to minimize the costs of switches and the reliability-oriented expenses. While the former includes the costs of installing and operating the switches, the latter consists of the distribution company’s lost revenue due to the undelivered energy and the regulatory incentives (or penalties) associated with service reliability indices. Two penalty-reward mechanisms are used to account for the financial benefits of increasing the service reliability through reducing the duration and frequency of interruptions. Proposing a novel reliability assessment model, we consider the possibility of malfunctions in both RCSs and FCBs in a highly efficient manner, which is a major contribution of this work. The proposed MILP model is applied to three test networks to validate its applicability and efficacy. The results show the importance of considering the possibility of switch malfunctions in distribution networks.

Optimal Sizing of a Wind-PV Grid-Connected Hybrid System for Base Load– Helsinki Case

Abstract: In recent years, due to the goal of decarbonizing energy systems, Renewable Energy Sources (RESs) have attracted attention as the primary potential energy resource in many countries. Thus, the utility-scale deployment of these resources has become of utmost importance. The large-scale connections and the intermittent as well as variable characteristics of these RESs cause challenges in maintaining a balance between power generation and consumption. Furthermore, supplying base load using RESs is another challenge for system operators. Hybrid RESs (HRESs), including solar and wind, together with energy storage, might be a remedy via which the resources can complement each other to some extent. In this paper, using geographical data acquired from National Solar Radiation Database and Matlab/Simulink, the output of each individual solar panel and wind turbine unit in the Helsinki region are calculated and used to optimally size an HRES to supply the base load. The results indicate that an HRES is at least 10.5 times more cost-efficient compared to a single RES system. Furthermore, it can be seen that, even in Finland where there is not sufficient solar radiation in winter, the size of the required energy storage system reduces by at least 13.4 times when an HRES is used.

Comparative Analysis of Three-Phase PV Grid Connected Inverter Current Control Schemes in Unbalanced Grid Conditions

Abstract: Recently, the regulation of photovoltaic inverters, effectively under imbalanced voltages on the grid, has been crucial for the operation of grid-connected solar systems. In this regard, determining the output current reference is an integral aspect of managing a solar inverter with an unbalanced voltage. Based on evaluations of Instantaneous Active-reactive Control (IARC), Positive Negative Sequence Control (PNSC), Balanced Positive Sequence Control (BPSC), and Average Active-reactive Control (AARC), this paper proposes a novel variable-current-t-reference calculation method for minimizing power fluctuations and current harmonics. The controller is employed to regulate both constructive and destructive sequences inside a static framework, therefore enhancing dynamic performance and facilitating the selection of suitable controls in the presence of significant network defects. This study also suggests comparing the four MPPT methodologies examined (fuzzy logic, current only, Incremental Conductance, and Perturb & Observe) to maximize energy output. The simulation results efficiently validate the suggested computation approach that is presented in the current reference.

Condition Assessment of Natural Ester–Mineral Oil Mixture Due to Transformer Retrofilling via Sensing Dielectric Properties

Abstract: Mineral oil (MO) is the most popular insulating liquid that is used as an insulating and cooling medium in electrical power transformers. Indeed, for green energy and environmental protection requirements, many researchers introduced other oil types to study the various characteristics of alternative insulating oils using advanced diagnostic tools. In this regard, natural ester oil (NEO) can be considered an attractive substitute for MO. Although NEO has a high viscosity and high dielectric loss, it presents fire safety and environmental advantages over mineral oil. Therefore, the retrofilling of aged MO with fresh NEO is highly recommended for power transformers from an environmental viewpoint. In this study, two accelerated aging processes were applied to MO for 6 and 12 days to simulate MO in service for 6 and 12 years. Moreover, these aged oils were mixed with 80% and 90% fresh NEO. The dielectric strength, relative permittivity, and dissipation factor were sensed using a LCR meter and oil tester devices for all prepared samples to support the condition assessment performance of the oil mixtures. In addition, the electric field distribution was analyzed for a power transformer using the oil mixtures. Furthermore, the dynamic viscosity was measured for all insulating oil samples at different temperatures. From the obtained results, the sample obtained by mixing 90% natural ester oil with 10% mineral oil aged for 6 days is considered superior and achieves an improvement in dielectric strength and relative permittivity by approximately 43% and 48%, respectively, compared to fresh mineral oil. However, the dissipation factor was increased by approximately 20% but was at an acceptable limit. On the other hand, for the same oil sample, due to the higher molecular weight of the NEO, the viscosities of all mixtures were at a higher level than the mineral oil.