Title of the Paper Prepared for BWP 2013

The Smart Green Concept:
Towards a Certified Smart System and a Green Renewable Market
Leontina Pinto
Engenho Pesquisa, Desenvolvimento e Consultoria Ltda.
Av. Cândido Portinari, 400 Rio de Janeiro/RJ Brasil CEP 22793-312
[email protected]
ABSTRACT
This paper introduces the smart green concept: the construction of a certified, renewable market
through the smart grid technology. We cover the theoretical foundations as well as the practical
aspects of the regulatory framework, the necessary technological support and system/market
interaction. A realistic case study applied to the Brazilian system highlights the potentiality of the
proposed approach.
Keywords: Research and Development (R&D), Smart grids, Green certificates, Clean Energy,
Regulatory Framework
1
INTRODUCTION
The clean energy dream is one of the humanity best-known claims. The balance between
emission reductions and a safe, reliable and economic energy supply is one of our hardest
challenges. However, it is not clear to most consumers the extent of their individual conservation
actions, and the value associated to corresponding results.
It is interesting to see that, in Brazil, emission reduction does not mean necessary load reduction.
As our energy matrix is mainly composed by hydropower, thermal units are only dispatched on
dry months, when complementary availability is needed; even on these situations, emission
depends on the used fuel: coal, gas, oil, etc.
2
OBJECTIVE
A sustainable use of energy requires therefore a careful study of expectations of system dispatch
– which, by its turn, depends on operative models, uncertainties, rules and regulations.
We suggest an R&D development targeting
 The combination of smart grids and green energy concepts into a more advanced
framework. The aim will be not only to minimize economic costs, but also the CO2equivalent emissions.
 The assessment of time and locational “pricing” signals, combining environmental and
economic aspects, to be instantly sent to consumers for personal information
 The implementation of a smart-grid/load management structure, where the consumer will
use these signals to monitor and control his own emission footprints, “interacting” with the
grid controller in order to optimize carbon management
 The evaluation of generator/consumer performances, in order to build a “green certificate”
framework, to be awarded to every agent able to meet their goals
3
THEORETICAL FOUNDATIONS
The basic pricing model requires that the optimal dispatch meets the load through the best use of
available resources. Its main difficulty lies on the uncertainties associated to generation capacity:
renewables (eolic, photovoltaic, hydro) are tied to climatological variability – uncertain by nature.
2.1 The optimal dispatch model
The optimal dispatch model consists therefore on the minimization of the expected operation
costs along a range of possible operation scenarios (representing possible generation outputs,
demands, etc.). The problem may generally be stated as
( (
( )
( )
( )
))
(1)
The traditional dispatch models xs as the operation variables in scenario s and E(c(xs)) is the
expected economical operation cost. However, this objective function can accommodate not only
economic costs, but include emissions or other subjective goals [1,2,3]. As the objective is the
emission minimization, this paper will use the minimum emission cost function, in order to
produce the indicators able to signal the consumers their carbon footprint.
Additionally, for each scenario s, A(xs)=ds
is the load balance equation. The associated
uncertainties include conventional consumption (residential, commercial, industrial, etc.) F(x)=qs
are system equality constraints (for instance, load flow equations, hydrothermal balances, etc.).
H(x)>ls represent generation system and equipment limits, including stochastic renewable
availability associated to joint and periodic climatological uncertainty (wind speed, solar radiation,
etc.) [4]
It is interesting to observe that dispatch is not “myopic”, and aims more than instant needs. As
long as some degree of storage is available, the objective is to optimize resources usage along a
given period. Horizon of optimization will depend on system’s availabilities and storages. For
instance, the dispatch of a large hydroelectric system may cover a mid- or long-term horizon; a
pure Eolic/PCH/PV system may cover a very short horizon, as a single day ahead.
2.2 Pricing and signals
Load marginal costs πd are defined as the system sensitivities to load changes. Following
problem stochastic characteristics, expected marginal emissions on bus i are given by
̅ ()
∑
(
( ( ( ))
)
( ( ))
(2)
where
̅ ( ) is the sensitivity of system expected emissions with respect to the demand on bus i
along operation scenarios s.
ps is the probability of scenario s
Expected load marginal emissions are associated to each bus and are therefore locational
signals, expressing the carbon costs added to the system as a consequence of an incremental
demand on this particular site. In other words, they may be seen as “drivers”, expressing the
“cleanness degree” of local consumption.
4
THE SMART GREEN CONCEPT
The smart green approach uses the smart grid technology and environment to pave the road for
a green consumption. This paper suggests two different applications, targeting the short- and
long-term green energy markets. However, there are many different possibilities, which would be
better exploit by a comprehensive research and development work.
4.1 The short-term green energy market
The construction of a short-term green energy market is still a challenge. There is, however, no
technical or theoretical obstacle to the design of an efficient short-term green market.
This goal may be achieved by a methodology illustrated in Figure 1 and summarized below.
Figure 1 – The smart green approach
1. At each time interval, the central or regional control center calculates the emission
marginal costs (2) for each consumer
2. Use the smart grid technology to send each participant consumer their emission costs
3. Collect each participant consumer’s decision about their carbon footprints – actions may
vary from load reduction to clean energy purchase
4. The consumer’s actions is “translated” into emission-equivalent impact by the optimal
model (1) – yielding each participant a certificate of corresponding reductions and adjusted
carbon footprint.
It is interesting to observe that the smart grid technology allows an almost instantaneous
communication between control center and consumers – who will probably be not willing to
constantly adjust their energy profiles and portfolios to everchanging conditions. An automated
set of responses, as well as possible “green consumption plans”, may perform the desired
actions independently, without the necessity of the consumer’s direct intervention [5]
4.2 The long-term green energy market
The long-term market deals with system expansion – that is, the clean energy is purchased on a
multiannual basis, creating funds for the construction of new plants. Within this framework, the
overall approach may be written as
1. Apply the optimal dispatch (1) to a long-term horizon operation problem (10-20 years),
considering the planned expansion, minimizing the operation emissions.
2. Evaluate the emissions associated to each bus and each participant’s share (in terms of
emissions and associated energy consumption)
3. Knowing their carbon footprint and associated energy, each participant will have the option
to adjust their portfolio, substituting their share of non-renewable energy for new, green
sources. This substitution will require the purchase of new energy, provided by new plants
to be constructed based on these long-term PPAs.
4. The authority in charge (ANEEL, EPE, maybe a new institution to be created) will evaluate
substitutions, consequent emisison reductions and certify the quality of each participant’s
use of energy.
It is interesting to see that this scheme creates the foundations of a new and efficient green
market, which may even be carried out by special auctions (regulated or de-regulated).
5
SOME SIMULATIONS
While this paper is still a proposal for a wider research, we offer some simulations that point out
the potentiality of the smart green framework. Table 1 presents the impact of each generation
source measured by incremental cost (US$/MWh) and incremental emissions (ton CO 2/MWh).
Hydroelectric energy comes from existing plants or new plants constructed under a nodeforestation concept, and are therefore considered clean (no significant emissions).
It may be seen that the cheapest source (coal) is responsible for the highest emissions. Minimum
cost and minimum emissions are therefore conflicting objectives, which must be correctly
assessed and discussed.
Table 1 – Thermal costs and emissions
Source
Coal
Diesel
Gas
Oil
2
Cost (US$/MWh)
76,11
341,11
115,56
161,11
Emissions (t CO /MWh)
0,90056
0,67192
0,48826
0,68931
Energy pricing is strongly driven by a sole indicator: submarket’s marginal costs, evaluated by the
Brazilian Chamber of Energy Trading as the result of an optimal operation program consistent
with formulation (1), searching the minimum cost dispatch (not the minimum emissions). The
program accommodates a five-year horizon, taking into account different possible future
hydrological scenarios. As current conditions could be biased to a higher impact (due to the
severe 2012-13 draught and therefore higher emissions), we chose a more conservative, wellbehaved 2011/2020 operation plan
5.1 The variability of carbon footprint
Iit is interesting to notice that hydroelectric availability varies along the time – and emissions will
vary accordingly: the higher the hydrologic availability, the lower emission levels. Figure 2
illustrates the fluctuation of the emission marginal cost along the time for the Southeastern
market for different possible future hydrological scenarios, as well as their expected value.
2
Figure 2 - Emission marginal costs (t Co /MWh), Southeast, 2011-2013
This illustration reveals a curious fact, not always perceived by society: emission marginal cost
may be, in certain scenarios, equal to zero. In fact, because of the system characteristics,
exceptional “wet” years may make it possible to supply the load exclusively with hydroelectric
power: thermal units are shut down, and emissions come close to negligible. A straightforward
conclusion – perhaps not intuitive for the general public – is that consumption decrease is not
necessarily equal to emission reduction.
5.2 The green surcharge: paying for clean energy
Many consumers cannot manage their load – their processes require a fixed, sometimes flat load
curve. It will be therefore necessary to design different management strategies, such as supply
replacement. Figure 3 displays the “green surcharge” necessary to ensure the consumer will
contract a smart green supply – in our case, replacing coal by gas. Again, different surcharges
express different source availabilities and electrical dispatch constraints.
Figure 3 – Green Surcharge, (US$/MWh), 2011
CONCLUSIONS
This paper proposes a Research and Development line targeting the construction of a new
green, renewable market. This market will certify not only the clean energy origin and source, but
also the energy carbon footprint of an interest consumer. The wide implications of the proposal
include an effective and reliable expectation of emission reduction, as well as the necessary
framework to support a clean energy expansion planning.
The strategies are based on realistic system and market representation. Therefore, incentives
and surcharges accurately express the gains and/or losses associated to load management,
ensuring a fair, transparent and reliable framework.
Many different extensions and applications, such as renewable incentives, risk management or
tariff building are straightforward and will be assessed in a near future.
REFERENCES
[1] L. M. V. G. Pinto and P. B. C. L. Leite, “Smart-grid, green energy and responsive consumers: a "smartgreen" framework”, IEEE PES PowerTech Trondheim, 2011.
[2] L.M.V.G.Pinto and P.Leite, “A new model for the optimal carbon management”, I Carbon Management
Conference, Orlando, 2012
[3] L.M.V.G.Pinto, P. Leite, L. Macêdo, “Um modelo para a expansão ótima multiobjetivo: conciliando aspectos
econômicos e ambientais”, XXI Seminário Nacional de Produção e Transmissão de Energia Elétrica, 2011
[4] J. Szczupak, L. Pinto, “Joint Hydro-eolic climatological scenarios generation”, Engenho Report, 2013
[5] L.M.V.G.Pinto, L.Nogueira, “The optimal expansion planning: the benefits of the renewable option”, IIEEE
nternational Conference on Environment and Electrical Engineering, 2013
BIOGRAPHY
Leontina Pinto was born in Coimbra, Portugal, in 1958. She graduated from the Federal University of Rio
de Janeiro (1958) and received her M.Sc. and D.Sc. degrees in 1981 and 1986 from the Graduate
Program (Systems and Electricity) of the Federal University of Rio de Janeiro.
Leontina was a researcher in the Brazilian Electrical Research Center and an Associate Professor at
Federal and Catholic University of Rio de Janeiro. She is currently the director of Engenho, a research,
development and consulting firm. Her interests include Energy Systems and Markets, Risk Management,
Scenario Forecast, Climatology and Clean energy frameworks.

Download Report