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El. knyga: iURBAN - Intelligent Urban Energy Tool

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iURBAN: Intelligent Urban Energy Tool introduces an urban energy tool integrating different ICT energy management systems (both hardware and software) in two European cities, providing useful data to a novel decision support system that makes available the necessary parameters for the generation and further operation of associated business models. The business models contribute at a global level to efficiently manage and distribute the energy produced and consumed at a local level (city or neighborhood), incorporating behavioral aspects of the users into the software platform and in general prosumers.

iURBAN integrates a smart Decision Support System (smartDSS) that collects real-time or near real-time data, aggregates, analyzes and suggests actions of energy consumption and production from different buildings, renewable energy production resources, combined heat and power plants, electric vehicles (EV) charge stations, storage systems, sensors and actuators. The consumption and production data is collected via heterogeneous data communication protocols and networks. The iURBAN smartDSS--through a Local Decision Support System--allows the citizens to analyze the consumptions and productions that they are generating, receive information about CO2 savings, and advises in demand response and the possibility to participate actively in the energy market. It also, through a Centralized Decision Support System allows utilities, ESCOs, municipalities or other authorized third parties to:

* Get a continuous snapshot of city energy consumption and production
* Manage energy consumption and production
* Forecasting of energy consumption
* Planning of new energy “producers” for the future needs of the city

Visualize, analyze and take decisions of all the end points that are consuming or producing energy in a city level, permitting them to forecast and plan renewable power generation available in the city
Preface xi
Acknowledgments xiii
List of Contributors
xv
List of Figures
xvii
List of Tables
xxiii
List of Abbreviations
xxv
1 Introduction
1(8)
Narcis Avellana
Sofia Aivalioti
2 Logic Architecture, Components, and Functions
9(26)
Alberto Fernandez
2.1 Logic View
11(21)
2.1.1 Local Decision Support System
11(2)
2.1.1.1 Handler data
13(1)
2.1.1.2 Business data
14(1)
2.1.1.3 Local decision support system user interface
15(1)
2.1.1.4 nAssist©
16(2)
2.1.2 Centralized Decision Support System
18(2)
2.1.2.1 Centralized decision support system central database
20(1)
2.1.2.2 Handler interfaces
21(2)
2.1.2.3 Business data
23(1)
2.1.2.4 Centralized decision support system HMI
23(2)
2.1.3 Smart Decision Support System
25(1)
2.1.4 Virtual Power Plant
25(2)
2.1.5 Smart City Database
27(3)
2.1.5.1 Digest component
30(1)
2.1.5.2 Open data API services
30(1)
2.1.5.3 Centralized decision support system database
30(2)
2.1.5.4 LDSS database
32(1)
2.2 Deployment View
32(2)
2.3 Conclusion
34(1)
3 Data Privacy and Confidentiality
35(14)
Alberto Fernandez
Karwe Markus Alexander
3.1 Confidentiality
37(1)
3.2 Confidentiality and General Security Requirements
38(1)
3.3 The iURBAN Privacy Challenge
39(4)
3.4 Privacy Enhancing via Transparency
43(1)
3.5 Privacy Enhancing via Differential Privacy
43(3)
3.5.1 Privacy-Enhancing Technologies Based on Privacy Protection
44(1)
3.5.2 Privacy Protection Implementation
45(1)
3.6 Conclusions
46(3)
References
46(3)
4 iURBAN CDSS
49(40)
Marco Forin
Fabrizio Lorenna
4.1 Introduction
49(3)
4.2 Graphical User Interface
52(1)
4.3 Main GUI Functionalities in Detail
52(35)
4.3.1 User Login
52(1)
4.3.2 Toolbar
53(1)
4.3.3 Management
54(1)
4.3.3.1 Map
55(2)
4.3.4 CityEnergyView
57(1)
4.3.4.1 EnergyView
57(1)
4.3.4.2 Filter Maker
58(3)
4.3.4.3 Graph Container
61(4)
4.3.4.4 Help Area
65(1)
4.3.4.5 Consumption 24H/7D/30D
66(3)
4.3.5 Demand Response Management
69(1)
4.3.5.1 DR program
70(4)
4.3.5.2 Peaks monitoring
74(3)
4.3.6 Tariff
77(1)
4.3.6.1 Tariff Plans
78(2)
4.3.6.2 Tariff comparison
80(1)
4.3.7 Diagnostic
80(1)
4.3.7.1 DataFlow Offline
80(1)
4.3.7.2 Hot Water Technical Losses
81(1)
4.3.7.3 Heating Technical Losses
82(1)
4.3.8 Weather Forecast
83(1)
4.3.9 User
83(2)
4.3.10 Configuration
85(1)
4.3.10.1 Console
85(1)
4.3.10.2 Controls
85(2)
4.4 Conclusion
87(2)
5 iURBAN LDSS
89(18)
Alberto Fernandez
5.1 Introduction
89(2)
5.2 Graphical User Interface
91(15)
5.2.1 Main Graphical User Interface Functionalities
93(13)
5.3 Conclusion
106(1)
6 Virtual Power Plant
107(18)
Mike Oates
Aidan Melia
6.1 Introduction
107(1)
6.2 Virtual Power Plant in iURBAN
108(2)
6.2.1 smartDSS
108(1)
6.2.2 LDSS
109(1)
6.2.3 CDSS
110(1)
6.2.4 VPP
110(1)
6.3 User Interface
110(4)
6.4 City Models
114(1)
6.5 Modeling Approach
114(1)
6.6 Case Study: Rijeka, Croatia
115(8)
6.6.1 "As is" Scenario
115(5)
6.6.2 "What if"---Scenarios
120(1)
6.6.3 Results
120(3)
6.7 Future Work
123(1)
6.8 Conclusion
123(2)
References
124(1)
7 iURBAN Smart Algorithms
125(20)
Sergio Jurado
Alberto Fernandez
7.1 Introduction
125(1)
7.2 "As is" Generation and Consumption Forecasts
126(10)
7.2.1 Introduction
126(1)
7.2.1.1 Random forest
127(2)
7.2.1.2 Artificial neural network
129(1)
7.2.1.3 Fuzzy inductive reasoning
130(2)
7.2.2 AI Generation and Consumption Forecast
132(1)
7.2.2.1 Model generation
132(1)
7.2.2.2 Model and prediction configuration parameters
133(1)
7.2.2.3 Grids and levels
134(1)
7.2.3 Development and Implementation
134(1)
7.2.3.1 Code
134(1)
7.2.3.2 Deployment
135(1)
7.3 Dynamic Tariff Comparison and Demand Response Simulation
136(6)
7.3.1 Functionality
136(1)
7.3.2 Stimulus/Response Sequence
137(1)
7.3.3 User Workflow
138(1)
7.3.4 Calculation Methodology
139(1)
7.3.4.1 Price elasticity background
139(1)
7.3.4.2 Dynamic tariff comparison and demand response formula
140(1)
7.3.5 Assumptions and Limitations
141(1)
7.4 Conclusions
142(3)
References
142(3)
8 Solar Thermal Production of Domestic Hot Water in Public Buildings
145(10)
Energy Agency of Plovdiv
8.1 Introduction
145(1)
8.1.1 The Pilot
146(1)
8.2 Public Solar Prosumers Background
146(1)
8.2.1 Background
146(1)
8.2.2 How Is the Energy Management and Monitoring Architecture Established?
146(1)
8.3 Case Study of a Prosuming Kindergarten
147(6)
8.3.1 Introduction
147(1)
8.3.2 What We're Interested in and How Data Can Tell It?
148(1)
8.3.3 What the Results Tell Us for Baseline and Post-retrofit Periods?
148(1)
8.3.3.1 What was happening when no energy efficiency measure was implemented back in 2012?
148(2)
8.3.3.2 What happened when the building was deeply renovated and RES was introduced in 2015?
150(1)
8.3.3.3 So how did EE and RES measures bring change in the kindergarten energy balance?
151(1)
8.3.3.4 What is the overall impact of becoming a prosumer?
152(1)
8.3.4 Discussion
153(1)
8.4 Conclusion
153(2)
9 Business Models
155(33)
Stefan Reichert
Jens Struker
9.1 Introduction
155(2)
9.2 Benefit Framework for the Operation of an Energy Management Platform
157(4)
9.2.1 Evaluation Framework
157(2)
9.2.2 Assessment of Benefits for Energy Providers
159(2)
9.3 Business Benefits for Related Use Cases
161(24)
9.3.1 Creation of City Energy View
161(2)
9.3.1.1 Testing and validation in the pilot of Plovdiv
163(4)
9.3.1.2 Testing and validation in the pilot of Rijeka
167(2)
9.3.2 What-if Scenarios
169(1)
9.3.3 Auditing/Billing
170(1)
9.3.4 Technical and Non-technical Losses
171(3)
9.3.4.1 Testing and validation in the pilot of Plovdiv
174(2)
9.3.5 Demand Response
176(1)
9.3.5.1 The model
176(1)
9.3.5.2 Regulatory environment
177(1)
9.3.5.3 No real economic benefit
178(3)
9.3.5.4 Demand response---lessons learnt
181(1)
9.3.6 Variable Tariff Simulation
182(1)
9.3.6.1 The model
182(1)
9.3.6.2 Testing and validation in the pilot of Plovdiv
183(1)
9.3.7 Consultancy Services
184(1)
9.4 Conclusion and Policy Implications
185(3)
References 188(1)
Index 189(2)
About the Editors 191(2)
About the Authors 193
Narcis Avellana, Alberto Fernandez
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