Centralized and decentralized control for demand response
Pacific Northwest Nat. Lab., Richland, WA, USA
DOI: 10.1109/ISGT.2011.5759191 Conference: Innovative Smart Grid Technologies (ISGT), 2011 IEEE PES
Demand response has been recognized as an essential element of the smart grid. Frequency response, regulation and contingency reserve functions performed traditionally by generators are now starting to involve demand side resources. Additional benefits from demand response include peak reduction and load shifting, which will defer new infrastructure investment and improve generator operation efficiency. Technical approaches designed to realize these functionalities can be categorized into centralized control and decentralized control, depending on where the response decision is made. This paper discusses these two control philosophies and compares their response performances in terms of delay time and predictability. A distribution system model with detailed household loads and controls is built to demonstrate the characteristics of the two approaches. The conclusion is that the promptness and reliability of decentralized control should be combined with the controllability and predictability of centralized control to achieve the best performance of the smart grid.
Available from: Innocent Kamwa
- "Authors in  classified these types of DR into two control categories: a centralized one in which we find DLC and a decentralized one that contains ARC. All types of DR control are applicable to medium and large industrial and commercial loads, but only dynamic pricing, DLC and ARC could be available for domestic appliances. "
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ABSTRACT: High penetration of renewable energy, bringing generation closer to consumption and splitting power system to micro grids after disturbances are trends and actions of our future power system. Thus, providing ancillary services will be a challenging task due to the lacks of conventional spinning reserves and storage devices. Demand response is an attractive way to provide ancillary services for smart grid due to its two way communication and costumer contribution in the control of the grid. This paper presents a centralized-decentralized control of responsive demand to enable primary and 10-min reserves. Two control technics are applied on each control mode: Multi-Band power system stabilizer and a droop control (DC). The effectiveness of these control strategies is demonstrated on the modified IEEE 14 bus system connected to a 14 bus distribution operating at 100% of its capacity and subjected to a severe loss of generation.
EIC Climate Change Technology Conference 2015, Montréal, Qc, Canada; 05/2015
- "Whilst decentralised control is prominent in current literature, there are compelling reasons to assess centralised control solutions as well. These have the benefit of being more controllable and predictable than decentralised systems at the cost of being less reliable and relatively slower (Lu et al., 2011). Current systems rely on centralised control methods making it likely that a future system will still contain both central and decentralised elements. "
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ABSTRACT: While pilot projects in the smart grid domain have abounded through public and private efforts, there is still uncertainty in identifying effective business models for the smart grid. In this paper we take the view of a new entrant in this market acting as a third party provider of demand response. New entrants are a key player in emerging technological domains but simulation and policy analysis from this perspective have not been forthcoming. We present a novel approach for evaluating business models within a regulatory context and avoid committing to specific technical solutions but instead embark on a parameter exploration through simple yet insightful agent-based models. Our simulations analyse the impact of system performance by three key variables; participant population size, household flexibility in terms of the maximum number of DR events allowed and size of load shifting/shedding available. The simulations indicate that benefits of avoided capital investment leads to valuing a participating household at approximately £1800 over a 20 year period. These results show how mandated infrastructure influenced by policy can affect the value proposition of a demand response service and provide a useful reference for system level parameter requirements. With weak business models, policy decisions can be crucial in providing the impetus needed to spur growth in this market.
Energy Policy 10/2013; 61:172-181. DOI:10.1016/j.enpol.2013.05.098 · 2.58 Impact Factor
Available from: desismartgrid.com
- "In this respect two business models are proposed in  namely, bilateral and pool based. Authors in reference  "
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ABSTRACT: Demand response (DR) has an important role to play in the electricity market for maintaining the balance between supply and demand by introducing load flexibility instead of only adjusting generation levels, at almost all operational time scales. There are many players in the market who benefit from DR, like the TSO, DSOs, retailers and end-customers themselves. The recent advent of smart grid technologies advanced the integration of DR by providing the needed information and communication infrastructure to the existing grid. Available literature on DR talks about the concept and definitions of DR, possible DR models for various region-specific market structures along with few DR implementation experiences in a system with ever increasing levels of loads along with evolution of innovative technologies like renewables, micro-grids, PEVs, etc. In this paper, the available literature on DR is categorized into general concept papers and papers on DR models applicable to the wholesale or retail markets, and are presented in a precise manner.
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