Identification of Value Proposition and Development of Innovative Business Models for Demand Response Products and Services Enabled by the DR-BOB Solution

Abstract

Abstract:The work presented is the result of an ongoing European H2020 project entitled DR-BOB Demand Response in Blocks of Buildings (DR-BOB) that seeks to integrate existing technologies to create a scalable solution for Demand Response (DR) in blocks of buildings. In most EU countries, DR programs are currently limited to the industrial sector and to direct asset control. The DR-BOB solution extends applicability to the building sector, providing predictive building management in blocks of buildings, enabling facilities managers to respond to implicit and explicit DR schemes, and enabling the aggregation of the DR potential of many blocks of buildings for use in demand response markets. The solution consists of three main components: the Local Energy Manager (LEM), which adds intelligence and provides the capacity for predictive building management in blocks of buildings, a Consumer Portal (CP) to enable building managers and building occupants to interact with the system and be engaged in demand response operations, and a Decentralized Energy Management System (DEMS ® , Siemens plc, Nottingham, England, UK), which enables the aggregation of the DR potential of many blocks of buildings, thus allowing participation in incentive-based demand response with or without an aggregator. The paper reports the key results around Business Modelling development for demand response products and services enabled by the DR-BOB solution. The scope is threefold: (1) illustrate how the functionality of the demand response solution can provide value proposition to underpin its exploitation by four specific customer segments, namely aggregators and three types of Owners of Blocks of Buildings in different market conditions, (2) explore key aspects of the Business Model from the point of view of a demand response solution provider, in particular around most the suitable revenue stream and key partnership, and (3) assess the importance of key variables such as market maturity, user engagement, and type of blocks of buildings as drivers to market penetration and profitability. The work presented is framed by the expected evolution of DR services in different market contexts and the different relationships between the main stakeholders involved in the DR value chain in different EU countries. The analysis also relies on the results of interviews conducted at the fours pilot sites of the DR-BOB project with key representatives of the management, operations, and marketing. These are used to better understand customer needs and sharpen the value proposition.

Custom Outlier Detection for Electrical Energy Consumption Data Applied in Case of Demand Response in Block of Buildings

“…Namely if the hourly energy consumption corresponding to the analyzed outlier is between the closed 75% range of a specific data cluster then the analyzed data point is not a real outlier and therefor the Score is set to 0, see (2). If the analyzed outlier data point is between the 75% range limit and a restrictive 90% data range limit, then there is a mild outlier and the score is set to 1, see (3). If the outlier hourly energy consumption value exceeds the restrictive 90% data range limit, then we have a real outlier data point and the score is set to 2, see (4).…”

“…Replication of the proposed approach and methodology can be considered, at least for the demand response effectiveness evaluation, to all range of consumers or prosumers, as the key performance indicators: 1—peak power reduction; 2—energy saving; 3—CO 2 reduction; 4—cost savings, etc. are easy to be applied on a clearly established baseline [ 3 ]. Demand response projects can be a great opportunity in local communities not only for the residential energy users, but also for the large pools of public buildings, belonging to the local authorities (schools), utility companies, chain of commercial buildings [ 4 ].…”

Impact of Demand Response on Optimal Sizing of Distributed Generation and Customer Tariff

“…Therefore, if the size of the data is large then the time taken by the central controller to issue the governing signals gets delayed whereas, in the case of decentralized control, each agent, i.e., customer, DGs, and BESS, defines their own load schedule which employs a multi-agent system (MAS) is presented in [71]. Buildings consume 40% of the total load [72], therefore, Nikos Kampelis et al [73] implemented a genetic algorithm (GA)-based optimization technique for EM in a building and used artificial neural networks (ANNs) prediction model for yielding DA power requirements of the customer. Time of use (TOU) pricing is used in this literature.…”

Modelling and evaluation of the Baseline Energy Consumption and the Key Performance Indicators in Technical University of Cluj‐Napoca buildings within a Demand Response programme: a case study

“…The studies in [14] developed a business model that involves several participants in the process of balancing the electricity market (energy suppliers, operators of electric energy distribution and transport, Energy Service Company (ESCOs), IT service providers, managers and building owners). In [15], the authors presented a tri‐layer decision‐making framework for strategic trading of DR aggregator, modelled as two bi‐level systems, to capture the interaction between different entities (energy market operator, customers etc.…”