Iker Diaz de Cerio Mendaza

Flexible Demand Control to Enhance the Dynamic Operation of Low Voltage Networks

Moving toward a carbon free energy system has become an objective for many countries nowadays. Among other changes, the electrification of strategic sectors, such as heating and transportation, is inevitable. As a consequence, the current power system load will substantially increase. In this context, the nature of the expected loads (heat pumps, plug-in electric vehicles, etc.) makes the low voltage (LV) networks specially targeted. A promising solution to overcome the challenges resulting from their grid integration is demand response (DR). This paper introduces a hierarchical structure for controlling the DR of a LV grid. This is designed to maximize the grid utilization, thereby reducing the need for reinforcement; accommodate the maximum number of flexible loads; and satisfy the power and comfort requirements from each of the consumers in the network. The validation of the proposed concept is made using a model of an LV network currently operative in Denmark. The results show that by using the proposed strategy a considerable improvement of the minimum system voltage and a better load distribution is obtained.

Generation of domestic hot water, space heating and driving pattern profiles for integration analysis of active loads in low voltage grids

The changes in the Danish energy sector, consequence of political agreements, are expected to have direct impact in the actual power distribution systems. Large number of electric boiler, heat pumps and electric vehicles are planned and will cope large percentage of the future power consumption at household level. Despite of the well-known flexible service that this kind of loads can provide, their flexibility is highly dependent of the domestic hot water and space heating demand and the driving habits of each user. This paper presents two methodologies employed to randomly generate thermal power demand and electric vehicle driving profiles, to be used for power grid calculations. The generated thermal profiles relied on a statistical analysis made from real domestic hot water and space heating data from 25 households of a typical Danish residential area. The driving profiles instead were formed based on conclusions derived from previous analysis on Danish driving patterns and the driving cycles extracted from the European project ARTEMIS. The obtained random profiles will allow electrical networks in Denmark to be analyzed while maintaining the probability of the original data set.

Smart Grid Constraint Violation Management for Balancing and Regulating Purposes

The gradual active load penetration in low-voltage distribution grids is expected to challenge their network capacity in the near future. Distribution system operators should for this reason resort to either costly grid reinforcements, use of low-voltage boosters, or demand response (DR) mechanisms. Since DR implementation is usually more cost effective, it is the favorable solution to avoid or delay the need for grid reinforcement. To this end, this paper presents a framework for handling grid limit violations, both voltage and current, to ensure a secure and qualitative operation of the distribution grid. This framework consists of two steps, namely a proactive centralized, and subsequently, a reactive decentralized control scheme. The former is employed to balance the 1-h-ahead load, while the latter aims at regulating the consumption in real time. In both schemes, fairness in terms of utilization of demand flexibility among the customers is incorporated. It is demonstrated that the proposed methodology aids in keeping the grid status within preset limits while utilizing flexibility from all flexibility participants.

Demand Response Control in Low Voltage Grids for Technical and Commercial Aggregation Services

The electrification of sectors such as heating and transportation represents a challenge as well as an opportunity for distribution system operators. On the one hand, they are committed to accommodate large numbers of highly rated loads in networks for which it was not designed. On the other hand, some of those represent a source of flexibility that can be used to satisfy different technical and commercial purposes. This paper introduces an upgraded hierarchical structure that aims to serve as a platform for activating and controlling the demand response in low voltage (LV) networks. In this way, a system operator playing a role of an aggregator not only could trade flexible demand in the power markets, but also materialize its energy agreements while ensuring the local network security and reliability. To verify the effectiveness of this extended method, a Danish LV network is considered. The results show that it is possible to fulfill energy commitments in energy markets such as the regulation power market while respecting the proper network operation. However, the activation of the flexibility offered might be limited depending on the network characteristics and the season of the year.

Intelligent control of flexible loads for improving low voltage grids utilization

Denmark has a plan to increase the amount of renewable energy in the power system in the coming decades. The wind power penetration is scheduled to increase to more than 50% by 2020. To cope with the high amount of fluctuating production, large power system reinforcements would be desirable. In order to limit the need for reinforcements the smart grid concept is getting a lot of attention. As a part of the whole concept, an active demand response is expected from end-consumers units like heat pumps and electric vehicles. This paper looks into the challenge of controlling these units in an intelligent manner. A centralized control strategy is developed at the secondary substation level in order to maximize the grid utilization thereby reducing the need for grid reinforcements. The simulation results show that the control strategy allows heat pumps and electric vehicles to charge in a time-effective manner while respecting the grid codes.

Optimum Aggregation and Control of Spatially Distributed Flexible Resources in Smart Grid

This paper presents an algorithm to optimally aggregate spatially distributed flexible resources at strategic microgrid/smart-grid locations. The aggregation reduces a distribution network having thousands of nodes to an equivalent network with a few aggregated nodes, thereby enabling distribution system operators (DSOs) to make faster operational decisions. Moreover, the aggregation enables flexibility from small distributed flexible resources to be traded to different power and energy markets. A hierarchical control architecture comprising a combination of centralized and decentralized control approaches is proposed to practically deploy the aggregated flexibility. The proposed method serves as a great operational tool for DSOs to decide the exact amount of required flexibilities from different network section(s) for solving grid constraint violations. The effectiveness of the proposed method is demonstrated through simulation of three operational scenarios in a real low voltage distribution system having high penetrations of electric vehicles and heat pumps. The simulation results demonstrated that the aggregation helps DSOs not only in taking faster operational decisions, but also in effectively utilizing the available flexibility.

Active control of thermostatic loads for economic and technical support to distribution grids

Active control of electric water heaters (EWHs) is presented in this paper as a means of exploiting demand flexibility for supporting low-voltage (LV) distribution grids. A single-node model of an EWH is implemented in DIgSILENT PowerFactory using a thermal energy balancing equation and three decentralized control schemes are designed to ensure consumer comfort, economic benefit to the consumer, and technical support to LV grids. First, a price-based control that adaptively adjusts an allowable energy band per electricity price is implemented to ensure economic benefit. Next, an adaptive update of the energy band is done based on feeder loading to respect thermal grid constraints. Finally, a voltage-based control is implemented to provide real-time voltage support to the LV grids. Simulation results demonstrate the capability of the presented method to realize both economic and technical advantages. For the given configuration and pricing scheme, EWH owners are able to decrease their electricity cost by 29.33%, along with simultaneous assurance of consumer comfort and grid constraints.

Enhancing the observability of traditional distribution grids by strategic meter allocation

Recent technological developments have facilitated the emergence of smart grids. In the smart grid perspective, metering and communication functionalities of the grid are greatly enhanced, thus boosting its observability and load management capabilities. This is an ideal scenario which is frequently encountered in contemporary literature. Unfortunately, in reality, operational and financial reasons have considerably obstructed the smart grid implementation. As such, current distribution grids still operate with reduced observability, implying that grid operators may have inadequate knowledge of load distribution in their networks, especially in real time. In this paper, the aforementioned problem is tackled by installing few measurements at strategic locations in low voltage distribution grids. By exploiting these strategic measurements, consumer load profiles, and state estimation techniques, the load distribution is approximated. As a result, the awareness of existent excess capacity in concurrent grids is augmented.

Probabilistic quantification of potentially flexible residential demand

The balancing of power systems with high penetration of renewable energy is a serious challenge to be faced in the near future. One of the possible solutions, recently capturing a lot of attention, is demand response. Demand response can only be achieved by power consumers holding loads which allow them to modify their normal power consumption pattern, namely flexible consumers. However flexibility, despite being constantly mentioned, is usually not properly defined and even rarer quantified. This manuscript introduces a methodology to identify and quantify potentially flexible demand of residential consumers. The procedure is based on non-flexible consumer clustering and subsequent statistical analysis. Consequently, the power consumption pattern of a flexible consumer is compared to a 3D probability distribution created by the previously referred methodology. The results show a strong relationship between the amount of potential flexibility and the probability of providing it. Finally, it is concluded that residential flexibility is most likely to be offered during transitions between summer and winter.

Alkaline electrolyzer and V2G system DIgSILENT models for demand response analysis in future distribution networks

Grid instabilities originated by unsteady generation, characteristic consequence of some renewable energy resources such as wind and solar power, claims for new power balance solutions in largely penetrated systems. Denmarks solid investment in these energy sources has awaked a need of rethinking about the future control and operation of the power system. A widespread idea to face these challenges is to have a flexible demand easily adjustable to the system variations. Electrothermal loads, electric vehicles and hydrogen generation are among the most mentioned technologies capable to respond, under certain strategies, to these variations. This paper presents two DIgSILENT PowerFactory models: an alkaline electrolyzer and a vehicle to the grid system. The models were performed using DIgSILENT Simulation Language, aiming to be used for long-term distribution systems simulations. Two voltage levels were considered: 20 kV for the electrolyzer grid connection and 0.4 kV for the plug-in electric vehicle. Simulation results illustrate the simplicity and manageability of the presented models.