Periodic Reporting for period 1 - DCNextEve (LV DC microgrids for evolved energy communities)
Reporting period: 2016-07-01 to 2018-06-30
The DCNextEve project was motivated by several societal needs such as climate change, energy efficiency and the central role of the consumer/prosumer (consumer and producer of energy) in our modern society. Expected direct benefits for society are related to a faster transition toward a resilient smart power grid, where ad-hoc interconnections between prosumers are easier to be explored in a DC configuration than in AC. DC low voltage microgrids (DCLVMGs) also respond better in case of technical, natural or human-made disasters, keeping islands of power grid functional, lowering also the impact of disruptions in other interconnected infrastructures. Indirect benefits are a higher efficiency for end-used energy, opportunities to increase consumption of self-production, and to reduce the impact of RES integration.
The major research objective of this project is the design and analysis of novel methods for management and control of multiple building-scale LVDCMGs operating on a defined territory, subsequently:
(a) to develop a holistic framework for design, modelling and control of clusters of LVDCMGs (the emerging communities of prosumers of tomorrow);
(b) to develop and validate models for typical elements of LVDCMGs;
(c) to develop and test models for optimal operation of clusters of DC microgrids under uncertainty;
(d) to develop and validate distributed control schemes for ad-hoc clusters.
(1): Several DC-native and DC-compatible appliances (e.g. typical appliances present in LVDCMGs-building level) were extensively studied through laboratory measurements tests and their behavioral electrical dynamical models were determined. These models are very useful for contingency analysis or for studying the behavior of DC microgrids under normal operation conditions (e.g. power flow).
(2) A generalized power flow model for DCMGs operating under droop control was proposed such that to directly take into account the nonlinear dynamical models of the loads derived at point (1). It was shown that this model is more accurate than state of the art DC power flow.
(3) Several key performance indicators (KPIs) were proposed for power quality assessment in LV DC grids. The objective of this work was to make useful qualitative and quantitative appreciations into the possible distortions that might appear when supplying loads in DC.
A breakthrough conclusion from the results of steps (1) to (3) is that all the devices tested were less sensitive to voltage fluctuations in DC compared to their supply in AC.
(4) Highly accurate and realistic daily load and generation power profiles for LVDCMG former were derived from real measurements. Aggregated real data for load (power consumption) and power output (energy produced) from RES installed at two prosumers situated in central and south Europe were analyzed and four characteristic load and power-output daily curves were extracted using an artificial intelligence clustering algorithm. This input was needed for the subsequent operation and control models of the LVDCMG clusters.
(5) A real-time energy management system (RT-EMS) was then developed that considered the load and RES production uncertainty characterized as expected time-series for the characteristic day of the year under study.
(6) A distributed, real-time and adaptive EMS (Adapt-EMS) was developed for the tertiary level control of an LVDC cluster considering the specific uncertainty level for each individual DC microgrid former (prosumer).
(7) The models developed in steps (5) and (6) were validated through simulations and real-time operation tests on a laboratory scale prototype LV DC microgrid cluster. Several operation use-cases were designed for the evaluation of the models and the tests were performed for several characteristic days of a year.
Under realistic assumptions, the results show benefits up to 9% yearly average savings due to local energy curtailment avoidance and exploitation of opportunistic savings due to price difference in energy coming from the grid. Furthermore, in DC LV-microgrid architectures the interplay between RES (e.g. rooftop photovoltaics - PVs), and storage (e.g. battery storage system - BSS) may become economically attractive.
- Several economic and societal benefits were identified from the role of prosumer as LVDCMGs former such as: potential to lowering the costs for connection to the grid, potential to reduce the costs of energy for the local consumers/prosumers, potential for innovative business models in a local energy-market, potential to reduce the energy curtailment from RES, increased local consumption of the energy produced locally and reduction in power losses in the associated power distribution systems.
- In terms of design and modelling of LVDCMGs, a comprehensive methodology for critical evaluation of technologies and architectures compatible with LVDC microgrids exhibiting technical and regulatory resilience was validated through extensive measurements and real-time operation tests;
- In terms of qualitative evaluation of technologies compatible with LVDC microgrids, several KPIs for power quality at low voltage DC grids were defined and evaluated. As a dissemination and exploitation perspective, with societal and technical impact, they were critically discussed with the Power Quality Measurement Methods Working Group from IEC and further progress towards standardization is expected;
- Adaptive, distributed schemes for operation and control of LVDC microgrids were validated through extensive simulations and real-time operation in laboratory-scale set-ups. They expanded the state of the art with innovative and efficient methods for estimating and bounding uncertainty coming from consumption and production of local energy. Adaptability and expandability are their major advantages compared with state-of-the-art methods.
These major contributions are expected to be of great interest to the research and academic community, as well as to the power system industry, such as DSOs and electrical energy providers, and especially to prosumers and the promoters of future developments of smart microgrids.