太陽光発電が導入された配電系統における蓄電池の最適計画・制御手法に関する研究

早大学位記番号:新8188早稲田大

Effects of household photovoltaic systems with energy storage systems on the voltage grid

This thesis was done to better understand and study issues caused by a high quantity of photovoltaic (PV) systems in low-voltage grids by the low voltage (LV) feeder simulations. Other focus was studying the effect of energy storage systems (ESSs) in avoiding issues and grid violations during high-level PV penetrations. PV modules are connected in series as a PV system to increase PV production. The value of PV production are determined by environmental parameters, mainly global irradiance received by solar cells and cell temperatures of the cells. Favoring a 3-phase grid connection and the optimal sizing of a PV inverter helps to maximize PV production and diminish issues caused to both the PV system and grid operations. PV systems as a part of the electrical distribution grid have been studied and tested for the last decades but a new trend of multiple PV systems located in the same feeder has caused grid issues. A peak production during the midday with low consumption in the feeder causes overvoltages in the grid points. Other issues caused by PV systems include thermal capacity limit violations in feeder lines due to reverse power flow (RPF), harmonic injections, voltage phase unbalances and undervoltages in grid components, like feeder lines and transformers. The level of PV production can be determined using the term hosting capacity (HC) based on different parameters. The parameter used in this thesis is the peak load of 180 kW of the feeder during the two simulation dates. The level of HC is restricted by lengths of feeder lines, number and capacity of loads and issues mentioned earlier. HC enhancement tools include voltage control, active power curtailment, reactive power control and transformer applications, like off-load and on-load tap changers. ESS technologies alter by price, capacity and operational attributes. Different algorithms and topologies of ESSs are chosen based on a wanted performance of PV systems. Centralized and de-centralized topologies of ESS are chosen depending on the number and separate shading conditions of the PV systems in the region. For household PV systems, battery energy storage systems are favored due to their physical size and lower investment costs when compared to other ESS technologies. In this thesis, the levels of PV capacity are simulated using Matlab Simulink program with the PV data measured in the solar PV research power plant in the Hervanta Campus of Tampere University. The PV data from the two simulation dates was added to the simulation model consisting of 21 residential buildings in the 285 m low voltage feeder. PV capacities were integrated starting from the transformer towards the end of the feeder in three cases. The operation states of ESSs were integrated with the settings of 2 %/min and 10 %/min ramp rate limits as the power control tool. In case 3, grid violations of the thermal capacity limit of feeder lines and +5% of the nominal overvoltages in distribution cabinets were recorded with over 120 % PV capacity. RPF was recorded at 77.69 % PV capacity. Grid violations could be avoided using de-centralized ESS applications in PV systems. The 2 %/min ramp rate limit would enable the LV feeder to avoid grid violations. But the 10 %/min ramp rate limit could not avoid grid violations on the other simulation date.Tämän opinnäytetyön tarkoituksena oli tutkia ja ymmärtää paremmin pienjänniteverkoissa suurissa määrissä esiintyvien aurinkovoimajärjestelmien aiheuttamia ongelmia. Toisena pääkohtana oli simuloida verkon sietokykyä ja energiavarastojärjestelmien vaikutusta verkkorajojen rikkoumisen välttämisessä korkean tason aurinkovoimatuotannon aikana. Aurinkovoimajärjestelmän tuotannon määrään ja laatuun vaikuttavat ympäristötekijät, pääasiassa aurinkokennojen saama valoisuus ja lämpötila. Aurinkovoimajärjestelmän moduulit voidaan järjestää eri topologioilla parantamaan saatavan tuotannon määrää. Kolmivaiheisen verkkoliitynnän ja invertterin optimaalinen mitoituksen suosiminen auttavat maksimoimaan tuotantoa ja vähentämään tuotannossa ja jakeluverkkotoiminnassa esiintyviä ongelmia. Aurinkovoimajärjestelmät eivät ole täysin uusi tai vähässä käytössä oleva teknologia, mutta uusi ilmiö on ilmaantunut sähköntuotannossa, jossa normaalisti kuluttajiksi profiloituneet kotitaloudet ovat hankkineet aurinvoimajärjestelmien moduuleja katoilleen kasvavissa määrin. Suuresta tuotannosta, erityisesti keskipäivisin, on syntynyt verkkohäiriöitä, yleisimpinä ylijännitepisteet ja verkkojohtojen kuumeneminen. Muita haittailmiöitä ovat käänteinen tehonvirtaus (englanniksi reverse power flow) tehonkulussa, harmoniset yliaallot, vaihejännitteiden epätasapaino ja alijännitteet. Tuotannon taso voidaan paremmin suhteuttaa verkkoarvoihin käyttämällä englannin kielen termiä Hosting capacity (HC). HC perustuu johonkin verkkoparametriin ja tässä työssä käytetään simulointimallin hetkellistä huippukulutusta 180 kW. HC:n arvoa rajoittavat johtimien pituudet, kotitalouksien kuormitusten arvot ja lukumäärä verkkohaaralla sekä aikaisemmin mainittujen haittatekijöiden arvot. HC:n arvoa kasvattavat työkalut ovat tehonhallinta, jännitteiden hallinta ja muuntajan jännitteen hallintalaitteet. Myös näiden hallintalaitteiden yhteiskäyttöratkaisuja käytetään paremmin kasvattamaan aurinkovoiman tuotantoa. Energiavarastojärjestelmät vaihtelevat investointihinnan, kapasiteetin ja käyttöominaisuuksien mukaan. Erilaisia järjestelmäalgoritmeja ja topologioita valitaan aurinkovoimajärjestelmien halutun suorituskyvyn perusteella. Kotitalouksien aurinkovoimajärjestelmien energiavarastojärjestelmien ratkaisuissa suositaan akkuteknologioita niiden pienemmän fyysisen kokojen ja alhaisempien investointihintojen vuoksi muihin teknologioihin verrattaessa. Tässä opinnäytetyössä aurinkovoimatuotantoa simuloitiin Matlab Simulink-ohjelmalla Tampereen yliopiston Hervannan kampuksen aurinkovoimatutkimuslaitoksessa mitatuilla arvoilla. Arvot lisättiin simulointimalliin, joka koostui 21 asuinrakennuksesta 285 metrin pienjänniverkkohaaralla kahden simulointipäivän aikana. Aurinkovoimakapasiteettia kasvatettiin kolmessa simulointitapauksessa rakennus kerrallaan alkaen muuntajalta. Verkkorajat menivät rikki yli 120% aurinkovoimajärjestelmän tuotannon arvolla 3. tapauksessa, jossa oli asennettu aurinkopaneeleja kotitalouksien, autotallien ja autokatosten etelänpuoleisille katoille. Verkkorikkomukset ilmaantuivat verkkohaaran alkupään johtimien ylikuumenisena ja loppupään jakelukaappien ja sulakekaappien ylijännitteinä. Vastaikkaisvirtaus tehonjakelussa havaittiin yli 77.69 % tuotannon arvoilla. Verkkorikkomukset pystyttiin välttämään käyttämällä energiavarastojärjestelmän asetusta perustuen tehon muutosnopeuden rajoittamiseen 2 %/min arvolla, mutta 10 %/min arvolla ei pystynyt estämään ylijännitteiden syntymistä verkkohaaran loppupään kotitalouksien sulakekaapeissa toisena simulointipäivänä

Optimization of Islanded Utility-Microgrids After Natural Disasters

Natural disasters can cause widespread disturbances/power outages within distribution networks and hinder a utility’s ability to provide uninterrupted power supply to the critical public buildings (e.g., hospitals, grocery stores, fire, police and gas stations) within the utility’s serviced region. Backup generators, which are typically relied on during power interruptions, have limited capacities and have been reported to experience failures during usage. Microgrids, defined as localized power grids that incorporate distributed generators (DGs) and energy storage systems (ESSs) to allow them to operate independent of the main grid (i.e., island mode), can help utilities provide disaster relief power supply to critical public buildings during such outages. This research investigates the optimization of utility-owned microgrids assumed to be operating in island mode and supplying power to a network of critical public buildings over the course of a week-long power outage. A deterministic and two-stage stochastic model (considering only DGs), as well as a multi-stage stochastic model (considering DGs and ESSs) are developed to optimize the investment economics, reliability and resilience of the microgrids. The models provides a holistic objective function that captures the investment, fixed operation and maintenance, power supply efficiency, reliability and resilience of the microgrid in terms of a minimized total cost to the utility. This is accomplished by optimizing the location, sizing, power supply assignment and total number of DGs and ESSs within a utility-owned microgrid. Hourly and weather (cloud coverage) uncertainty in daily DG power output and critical public building demand are considered. The final DG-plus-ESS multi-stage model provides an exhaustive solution framework, that analyzes the microgrid’s reliability across all possible weather (cloud coverage) scenarios (e.g., sunny, cloudy, overcast) of a week-long outage (3,279 total scenarios)

False Data Injection Impact on High RES Power Systems with Centralized Voltage Regulation Architecture

The increasing penetration of distributed generation (DG) across power distribution networks (DNs) is forcing distribution system operators (DSOs) to improve the voltage regulation capabilities of the system. The increase in power flows due to the installation of renewable plants in unexpected zones of the distribution grid can affect the voltage profile, even causing interruptions at the secondary substations (SSs) with the voltage limit violation. At the same time, widespread cyberattacks across critical infrastructure raise new challenges in security and reliability for DSOs. This paper analyzes the impact of false data injection related to residential and non-residential customers on a centralized voltage regulation system, in which the DG is required to adapt the reactive power exchange with the grid according to the voltage profile. The centralized system estimates the distribution grid state according to the field data and provides the DG plants with a reactive power request to avoid voltage violations. A preliminary false data analysis in the context of the energy sector is carried out to build up a false data generator algorithm. Afterward, a configurable false data generator is developed and exploited. The false data injection is tested in the IEEE 118-bus system with an increasing DG penetration. The false data injection impact analysis highlights the need to increase the security framework of DSOs to avoid facing a relevant number of electricity interruptions

Adaptive OCR coordination in distribution system with distributed energy resources contribution

More and more distributed energy resources (DERs) are being added to the medium-voltage (MV) or low-voltage (LV) radial distribution networks (RDNs). These distributed power sources will cause the redistribution of power flow and fault current, bringing new challenges to the coordination of power system protection. An adaptive protection coordination strategy is proposed in this paper. It will trace the connectivity of the system structure to determine the set of relay numbers as a tracking path. According to the topology of the system structure, the tracking path can be divided into two categories: the main feeder path and the branch path. The time multiplier setting (TMS) of each relay can be used to evaluate the operation time of the over-current relay (OCR), and the operation time of the relay can be used to evaluate the fitness of the TMS setting combination. Furthermore, the relay protection coordination problem can be modeled to minimize the accumulated summation of all primary and backup relay operation time (OT) subject to the coordination time interval (CTI) limitation. A modified particle swarm optimization (MPSO) algorithm with adaptive self-cognition and society operation scheme (ASSOS) was proposed and utilized to determine TMS for each relay on the tracking path. A 16-bus test MV system with distributed generators (DGs) will be applied to test the adaptive protection coordination approach proposed in this paper. The results show that the proposed MPSO algorithm reduces the overall OT and relieves the impact on protection coordination settings after DG joins the system. The paper also tests and compares the proposed MPSO with other metaheuristic intelligence-based random search algorithms to prove that MPSO possesses with increased efficiency and performance

Reactive power control in photovoltaic systems through (explainable) artificial intelligence

Across the world, efforts to support the energy transition and halt climate change have resulted in significant growth of the number of renewable distributed generators (DGs) installed over the last decade, among which photovoltaic (PV) systems are the fastest growing technology. However, high PV penetration in the electricity grid is known to lead to numerous operational problems such as voltage fluctuations and line congestions, which could be eased by utilizing the reactive power capability of PV systems. To this end, we propose to use artificial neural network (ANN) to predict optimal reactive power dispatch in PV systems by learning approximate input–output mappings from AC optimal power flow (ACOPF) solutions in either a centralized or a decentralized manner. In the case of decentralized control, we leverage Shapley Additive Explanations (SHAP), an explainable artificial intelligence (XAI) technique, to identify non-local grid state measurements which significantly influence the optimal dispatch of each individual system. Both centralized and decentralized ANN-based controllers are evaluated through a case study based on the CIGRE medium-voltage distribution grid and compared to baseline control strategies. Results show that both ANN-based controllers exhibit superior performance, hindering voltage problems and line congestions which are encountered with baseline strategies while recording an energy saving of 0.44% compared to fixed power factor control. By leveraging ANN and SHAP, the proposed decentralized controllers for reactive power control are able to achieve ACOPF-level performance while promoting data privacy and reducing computational burden

Defending against Distributed Denial of Service Attack Under Tunnel Based Forwarding

Today, attacks are a harmful element of the computer networks. Distributed Denial of Service (DDoS) attack is one of the most harmful attacks. Many defense mechanisms have been proposed to mitigate the effect of the attacks. 2In this thesis, we study two methods for defending against DDoS attacks. First, we identify the attack packets to detect a DDoS attack by checking the TTL value of incoming packets and monitoring the number of new source IP addresses of incoming packets. Second, we propose an algorithm to traceback the attack traffic to identify the source IP address of origin by deploying a tunneling based protocol. The tunneling based protocol is called the Locator/Identifier Separation Protocol (LISP) and it is deployed in a domain network to encapsulate all outgoing packets decapsulate all incoming packets. As a side-effect the tunneling protocol reveals the ingress point of attack traffic. We also analyzed the approach in a simulation environment and compare the results in the domain network when deploying the tunneling based protocol