Publications

1) Vovos, P. and Georgakas, K. (2021) "Smart Boilers: Grid Support Services from Non-Critical Loads" in Journal of Power and Energy Engineering, 8 ,pp. 23-45. doi: 10.4236/jpee.2020.812003.

2) Maryam Mohiti, Mohammadreza Mazidi, David Steen, Le Anh Tuan (2022), "A Risk-Averse Energy Management System for Optimal Heat and Power Scheduling in Local Energy Communities " in IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), Prague, 28 June-01 July 2022.

3) Dimitrakakis, G., Georgakas, K.; Topalis, E.; Vovos, P. (2024) Grid Quality Services from Smart Boilers: Experimental Verification on Realistic Scenarios for Micro-Grids with Demand-Side Management Oriented to Self-Consumption" in  Energies,17, no. 9: 2096. https://doi.org/10.3390/en17092096

Events

1) "PV generation self-consumption", a presentation by Panagis Vovos for Patras IEEE Student Branch web-conference on "Smart Cities and modern Photovoltaics", May 2021. 
Click here to watch (in Greek) !

2) "Patras IQ 2023 (prefectural innovation conference) ", Workshop on Technological Results of SUNSETS project, Patras, 26-28 November 2024 

Reports

1) Simulation of Smart Boilers (D2.1), July 2021.
Deliverable D2.1 is the written report on the output provided by Task 2.1, which contains the operating analysis of Smart Boilers (SBs) through simulations. The ability of smart boilers to precisely control active power, thus heating rate, and simultaneously control reactive power and harmonic content will also be verified. The following activities will be carried out:
• Analysis of SBs through simulation with valuable inputs from similar converter analysis after close cooperation between UoP and UoPe.
• Modelling of the thermal response of the boiler with input data and parameters provided by Maltezos SA (subcontractor).
• Integration of SBs models with thermal response to achieve a complete simulation model of SBs.
The outcome is a complete, advanced, simulation tool for SBs, providing both their electrical and thermal response. 

2) Ancillary service definition (D3.1), October 2021.
In this report, a list of the ancillary services (AS) which can be provided to distribution system operators (DSOs) is defined, focussing on ancillary services provided by PV-dominated near-zero energy building (NZEB)-based local energy communities (LECs).
The defined ASs aim to allow larger PV penetration in distribution networks without affecting its secure operation. Initially, a literature study on the ASs that can be provided in system operators is performed, emphasising on the services that allow larger exploitation of solar energy for provision of services to distribution systems. Subsequently, the concise list of ancillary services is determined, and each service is analysed in detail, along with their impact on solar energy and distribution networks. Then, the feasibility and the applicability of the defined ancillary services, as well as their value to distribution system and their ability to facilitate increased PV penetration in distribution level is evaluated.
The outcome of this report will be used by Task 3.3 for the validation of the defined ancillary services in simulation environment as well as by Task 6.2 for the real-life demonstration of the ancillary service on Chalmers campus network.   

3) PV and load forecasting algorithms (D4.1), February 2022.
In this report, the developed forecasting algorithms for PV generation and electricity load of the local energy communities (LECs) are described. The forecasts will be as input to the energy management system of the LECs for short term (intra-day and near real time) and long term (day-ahead) management of their resources. Accordingly, advanced machine learning and mathematical based forecast algorithms with two different time steps of 1-hour and 15-minutes and three horizons of 15-minutes, 1-hour and 24-hours ahead are provided for the prediction of the PV sites and building loads of the LEC located at Chalmers University of Technology campus. To develop the machine learning algorithms, measurement data of PV historical production is collected form Chalmers campus PV sites, while weather variables are retrieved from numerical weather prediction models. For each forecasting algorithm based on the data, an individual forecasting method is presented; the forecast for short term and very short term (1-hour ahead and 15 minutes ahead) are based on dynamic recurrent neural networks (DRNNs) while the 24-hours ahead Artificial Neural Networks and hybrid methods are utilized for the prediction. Mathematical based algorithms such as state-space model identification regression method (SSIR) are also performed as comparison and a potential algorithm for the forecast system.
The results of the presented methods are validated and compared with the other state-of-art methods, which show the superiority of the presented method. Furthermore, the realization and future exploitation of the forecast system is briefly described. The presented forecast methods utilize weather prediction variables instead of measurements. Therefore, the accuracy of the weather predictions highly influences the accuracy of the PV forecast. However, their results are more viable in real time exploitation where real weather measurements are not available. The results of the algorithms on the real-life data of two PV sites and building loads in three mentioned time horizons at Chalmers are presented, to illustrate the accuracy and effectiveness of the provided methods. The short term and very short-term prediction show high accuracy while the accuracy decreases as the forecast horizon increases to 24 hours ahead. The presented forecasting tool of this report will be used in as input to the EMS of Task 3.3 as well as Task 6.2 for the real-life demonstrations. In SUNSETs the output of the forecasting system will be used in short term (intra-day and near real time) and long term (day-ahead) management of resources. Consequently, the forecasts are provided in two different time steps of 1-hour and 15-minutes with three horizons of 15-minutes, 1-hour and 24-hours ahead.

4) ​Optimization model of suggested PV system for NZEBs with respect to electricity costs and PV self-consumption (D7.1), April 2022 .
In this report we will study the optimal management of energy-consumption in a photovoltaic system with energy storage, through an optimization algorithm, which incorporates optimal scheduling of controlled loads, with a view to maximum net profit and minimum energy costs. In particular, we look at the latest measures of Greek and Swedish PV development using net metering systems. In such, the energy generated by the photovoltaic system can be either consumed by local consumers or fed into the grid. The final cost shown in the electricity bill depends on the energy produced by the photovoltaic system, the energy consumed by the grid and the energy fed into the grid. Therefore, the actual electricity consumption is very important for estimating the benefits of using this renewable energy source.

5) Test cases and demonstration plan (D6.5), May 2022.
In this report the outcome of the work in Task 6.5 is presented. The test cases that can be demonstrated different test sites of SUNSETS will be presented, along with the most significant technical requirements related to their implementation. The test cases are prioritized based on their significance and applicability, while the technical requirements include the required resolution of the measurements. . Furthermore, the devices that will be controlled in each test case have been identified. Therefore, it is expected that the definition of the test cases and the respective technical requirements specification will facilitate the preparation of each test site for the demonstration activities.

6) Assessment of Hardware and Control of Smart Boilers (D2.2), July 2022.
In this report a description of the hardware built so far for the purposes of the project and the device control aspects are presented. This presentation comes in a brief extend and does not enter in details related to all the considerations prior to the final component and design choices, with the exception of Section 3, where the reasons for selecting the specific topology are analyzed.The report opens with a general presentation of the energy storage and grid quality services concepts as realized with a Smart Boiler. It is explained that a non-critical load as a boiler is the perfect candidate to support these goals, under the currently configured conditions where the increased penetration depth of the renewables leads to unpredictable fluctuations of active/reactive power flow and potentially unacceptable harmonic distortion of the voltage waveform.Then follows the explanation of selecting a single switch topology with DC input voltage as rectified from the mains. This discussion highlights the advantages of the converter, which are simplicity, robustness, reliability, endurance, small size, low cost and high efficiency.
The main part of the report is dedicated to a one-by-one presentation and description of the several components that form the converter at both the power circuit and the control circuit. Features and specifications of these components together with power loss calculations and measurements, time response oscillograms and other technical details fully justify the design options of the research team and offer a comprehensive illustration of the device operation and performance.Next, the main aspects of the closed loop control strategy are presented, with graphs and block diagrams to clarify the background design and the resultant performance.
Some thoughts about future work are listed at the closing of the manuscript.  

7) Ancillary Service Provision (D3.3) 
In this report a framework is presented in which Local energy communities (LEC) can participate in providing ancillary services namely congestion management and voltage control. In the proposed framework the LECs offer flexibility in terms of active and reactive power to the DSO to relieve congestion and keep the voltage in the predefined range. It is shown that in the proposed framework the LECs increase the distribution network (DN) flexibility by providing the ancillary services while their financial benefits is increased.  It is also investigated that the LECs can contribute to increasing to the PV penetration by providing flexibility in case of congestion sourced by the reverse power flow of the Renewable energy sources i.e., PV generation. In the proposed framework an efficient trading mechanism for offering flexibility for congestion management and reactive control is introduced and the price of the services is also obtained. The financial benefit for the LECs along with the operational benefit of the DSO is illustrated. The power quality of the DN feeder supplying the LEC is increased by the harmonic management control of the smart boilers. Chalmers campus network is considered as the test case and the real data from its facilities are utilized to develop the software tools.    

8) Economic Impact from the Conversion of PV to Thermal Energy at SBs (D7.2) 
In this report we deal with the economic analysis based on well-known indicators as Net Present Value (NPV) and levelized cost of electricity. We will evaluate the profitability of the suggested PV system by considering the discounted cash flow methodology, which is a valuation method that considers only cash inflows and outflows. Furthermore, in order to evaluate the economic impact of energy storage from conversion of PV electric power to thermal energy at smart boilers, several linear models are going to be used analyzing time series data regarding load declarations, energy losses, energy prices, renewable loads etc. The main objective is to develop tools for the evaluation of the financial impact of combining thermal and battery storage with PV systems.    

9) Smart Boiler as PV Self-Consumption Device (D2.3) 
The present report summarizes the latest improvements regarding the hardware and software structure of the Smart Boiler device and outlines the principal considerations relevant to the certification process for the final setup that is expected to be complete by the end of the project. In the first part of the report (Sections 2 and 3) there is a brief description of significant hardware/software improvements, that came up as a result of the consistent efforts of the UoP and UoPe research teams towards the optimization of the SB performance, regarding the control response time constants, system reliability, active/reactive power capability, range of harmonic content mitigation, efficiency, size, EMI footprint to the surroundings, bill of materials and several other critical parameters. With the support of some rich material, that includes photos, circuit schematics, simulation graphs, software environment screenshots, oscillograms, flowcharts, tables etc., some of the main aspects of the work carried out by UoP and UoPe are presented in a descriptive manner. In the second part of the report (Section 4) the authors attempt to list in a brief manner the main aspects of the device certification process for the SB kit, the basic hardware requirements for the kit to successfully pass the tests to be conducted and the subject of some of these tests.    

10) Resources remuneration (D3.4) 
This deliverable proposes a tool to distribute the financial benefit of the LECs among the community members according to their contribution to the ancillary services. Accordingly, the extent that each resource has contributed to the respective services will be quantified, and in the real-time if their promised delivery is not validated, they will be penalized, and the final payment will be based on both contribution and imposed penalties.    

11) Optimal allocation of resources (D4.2) 
This deliverable aims to determine the optimal sizes of the controllable resources i.e. PV panels, battery energy storage (BES), Thermal energy storage (TES), and smart boiler (SB). The resource allocation problem is formed as an optimization problem by modelling the different resources. Investment cost of the resources are presented with capital expenditures (CaPEX) and operation expenditures (OpEX) and several case studies are carried out to compare the results of different local energy communities (LECs) and energy prices. To make the study valid for future exploitation a sensitivity analysis to CaPEX and OpEX values of the resources are conducted. Furthermore, possible LECs are chosen within Chalmers distribution network and dynamic clusters are formed to enable resources to participate in the ancillary provision.      

12) IOT platform specifications (D5.1 and D5.2) 
This report will act as the documentation along with instructions for the IoT platform developed in WP5. the development of the IoT platform and the interface with all the required devices that will be controlled in SUNSETS will also be explained.    

13) Coordination Committee minutes of meetings (D1.1) 
Taking meeting minutes is essential to a meeting. Meeting minutes will be kept from the Chairman of the Coordination Committee, in order to inform all partners of what happened during the meeting and define the next step planned. Before each meeting the different topics to be addressed during the meeting will be prepared and distributed to the members of the committee. During each meeting, minutes will be appropriately written and distributed quickly after the meeting, so that everyone knows their next tasks or actions. Actions from remarks will be differentiated, as well as the different actions per partner with a deadline. Minutes will be organised in a logical manner and not chronologically. A short summary organized per partner and per task at the end of the minutes will assist partners to quickly glance at the minutes and spot the actions they need to realize quickly. The Chairman was responsible to prepare a summary of all the minutes of the meetings at the end of the project. This report includes those minutes.    

14) LEC scheduling & real-time dispatching (D4.3) 
This report presents a comprehensive overview of the developed optimization and real-time integration framework designed to deliver ancillary services as defined in SUNSETs. It provides detailed insights into the demonstration sites and outlines the planned sections where the demonstrations will take place. Additionally, the controllable devices utilized in the demonstration cases are explained, along with the way they are controlled in the demo cases. The measurement and communication systems of different devices at various locations are described, with a specific focus on the software gateway. This gateway facilitates communication between Chalmers campus, the smart boiler located at the Greek site, Azelio thermal energy storage, and Chalmers emulated IoT platform. The report delves into the specific functions and tools integrated within the optimization framework to provide ancillary services at different time scales. Moreover, it elucidates how the setpoints and initial status of the devices are exchanged within the framework, enabling the real-time demonstration of the various cases. The report serves as a comprehensive guide to understanding the intricacies and capabilities of the optimization and real-time integration framework in delivering ancillary services for SUNSETs.    

15) Demonstration of Smart Boiler Operation (D6.1) As the research project progressively reaches to an end the Greek research team finalized the configuration of the demo site at the campus of UoP and conducted several experiments to validate the even and effective operation of the SB device, according to the preset specifications, in several test case scenarios. In this report the outcome of the work in Task 6.5 is presented. The demo site infrastructure, with all its controllable devices, metering units and necessary equipment is described in details. Other technical details presented include the required resolution of the metering equipment. The conducted tests verified the proper operation of the SB scheme for the energy storage while offering quality services to the grid and selected results are presented in the form of graphs and oscillograms.     

16) Conversion loss reduction demo (D6.3) 
The aim of this deliverable is the investigation of the possibility of exploiting the thermal storage residual heat conversion losses of AZELIO’s TES.POD in a virtual demonstration environment. The main objective is to investigate how much overall energy efficiency improvement can be achieved when electricity output is kept at maximum efficiency by the exploitation of the H2E conversion losses on a non-critical thermal load. The specific fields of research include: the development of data and communication linking between Azelio’s site (as described in D6.2, where the Stirling engine cooling system will be monitored), and UoP’s site (as described in D6.1, where extracted heat will be emulated and fed to the boiler’s circuit), once the virtual demonstration environment has been established, tests will be performed on the performance of the TES.POD when the residual heat in the H2E conversion process (i.e., by Stirling engine) will be utilized to heat the incoming (cold) water to the smart boilers, simulating the heat consumption of specific demand, evaluate the improvement of the overall energy consumption performance when the residual heat from the Stirling engine is coupled with the thermal system of the demand. This report practically contains all the above points, aiming at verifying the performance of combined H2E and smart boilers technology.    

17) Demo evaluation (D6.4) 
The primary aim of this task is to evaluate the demonstration activities carried out in WP6 of SUNSETs project. This evaluation involves comparing the obtained demonstration results with the project's defined objectives and expected outcomes. To facilitate this assessment, a methodology is introduced to establish Key Performance Indicators (KPIs), which serve as measurable metrics for evaluating the demonstration results. The report outlines the detailed approach used to calculate the KPIs for each use case of the demonstrations, allowing for a comprehensive scoring of the use cases based on their performance. Furthermore, the deliverable will identify the results that hold potential for dissemination through appropriate scientific and industrial communication channels. These valuable findings will be shared as inputs to WP7, contributing to further analysis and utilization within the project.    

18) Impact of energy price variability (D7.3) 
In this report we deal with the integration of photovoltaic (PV) systems into the grid which today involves new and competitive ways to achieve this. Consequently, there is a need to devise methods that not only include energy calculations, but also incorporate financial and financial feasibility characteristics. According to literature research, there are several tools available to carry out a productivity study of a photovoltaic system. However, certain shortcomings have been identified, be it in the definition of the economic-financial scenarios or in the results obtained, because they do not provide all the basic information, do not use all common economic criteria or, in some cases, understand the difficulties and training requirements for correct use discourage use. It is for this reason that this work recommends a complete methodology that can be used as a pre-selection tool prior to designing a photovoltaic self-consumption system in Honduras. The realistic access data make it possible not only to obtain results for the usual criteria of economic and financial sustainability (net current value, internal interest rate, discounted amortization period and net cash), but also allow an assessment of competitiveness on the basis of the levelized electricity price (LCOE). In addition, the new criterion of the direct costs of self-consumed photovoltaic electricity is included.    

19) Exploitation of project solutions (D7.4) 
This deliverable aims to evaluate the business potential of the technical solutions developed within SUNSETS. It will secure that the solutions which have been positively validated for their business and technical potential during the demonstration activities, will result in a credible business plan for commercialization and have resources and value‐chain for the next TRL beyond project. To accomplish this aim use cases i.e., results of the project are defined and their TRs are determined. The Key Exploitable Result method is used to evaluate the use cases. The exploitation strategy is developed by introducing a preliminary exploitation plan for each of the uses case which in the required actions and the role of each partner is determined. Furthermore, a business plan for the use cases with TRL 6 and above are developed to facilitate the market entrance of the exploitable solutions.