Energy planning in India

The hidden cost of a suboptimal energy design

The most expensive decisions in an energy project are made early, on little data, and then locked in for 20 to 30 years. When the design is wrong, nothing breaks. The DG sets start, the chillers run, the bills get paid, and nobody ever sees the more efficient and more reliable system that was never built.

See what the audits found ↓

Why the cost stays hidden

A badly chosen system doesn't fail. It runs.

An oversized DG set still starts when the grid trips. A chiller plant sized on a rule of thumb still keeps the building cool. The extra cost shows up as capital that sits idle, fuel burnt at poor part load and fixed charges on demand that never comes, every year, and no invoice ever names it.

Diesel backup is the clearest example in India. When the Centre for Science and Environment studied five residential societies, it found DG sets now running only 200 to 300 hours a year. Spread the cost of the set over those few hours and every unit it generates becomes very expensive. At a commercial office in Ranchi, a 30-day log of 1,000 kVA of DG capacity showed the sets in use 1.3% of the time.

Most sites never put a number on this. The DG set is simply part of the building, and its cost is buried in the flat price or the project capex.

Diesel backup: what the studies found

₹27–33: per kWh from DG sets once the set's capital cost is included, against ₹16–17 for fuel and running alone (CSE, five residential societies)
1.3%: of the time that 1,000 kVA of DG capacity was used at a commercial office in Ranchi, over a 30-day log (GIZ)
20%: better fuel efficiency for a 500 kVA set at 75% load than at 25% load (ICF for Shakti Foundation)

Sources: CSE, Solar Rooftop: Replacing Diesel Generators in Residential Societies (2017); GIZ, Diesel Study Report (2023); ICF for Shakti Sustainable Energy Foundation, Diesel Generators (2014)

Looking back

Where someone took the time to look

Most energy systems are never checked against the assumptions they were built on. Now and then the Comptroller and Auditor General of India goes back and compares the decision with what actually happened. Here are two of those reviews, both in Delhi.

Commonwealth Games 2010: power backup nobody compared

₹112 crore

spent on DG sets, UPS and generator hire for the Games venues. The permanent DG sets went in without a cost-benefit analysis, CAG found.

For the 2010 Games, CPWD installed DG sets at five stadiums as permanent fixtures. At Jawaharlal Nehru Stadium it assessed the critical load at 10,065 kVA and installed 11,530 kVA of DG sets, plus 3,330 kVA of UPS. Across the venues, DG sets, UPS and hired generators came to ₹112.29 crore.

CAG found that CPWD "installed all DG Sets in all the five venues as permanent fixtures without any cost benefit analysis (permanent fixtures v/s hiring as and when required)", even though the consultant had suggested plug-in or permanent generators. It called the result "a massive 'overkill'" in October, a lean month for power demand, with an extra 100 MW allocated to Delhi for the Games. The prospects of using the sets after the Games, it said, were "negligible". CPWD replied that the configuration was meant to be finalised at technical sanction and that the plug-in option was intended for temporary overlays.

National Gallery of Modern Art: twelve years on one demand assumption

6–34%

of contract demand actually used from April 2018 to March 2022. CAG found ₹1.97 crore of the fixed charges avoidable.

The gallery's electricity connection was sanctioned in March 2010 at 3,176 kW, with a contract demand of 3,176 to 3,970 kVA depending on power factor. Over four years, actual consumption ranged from six to 34 per cent of that contract demand, and the gallery paid ₹3.95 crore in fixed charges.

CAG found that "had the load been reduced to 50 per cent of initial sanctioned load, NGMA could have saved half the amount paid for fixed electricity charges, i.e. ₹1.97 crore". NGMA said the load was needed for the climate control that protects the artworks, on CPWD's advice; the Ministry of Culture told it to resolve the discrepancy. Not every cost of a design sits in the plant room. A demand assumption made once also gets locked into the tariff.

The common thread

Not a lack of expertise. A lack of options compared.

None of these projects lacked engineers or consultants. The audit reports don't tell us everything about how each decision was made, but they point to familiar limits of conventional planning:

A limited set of variants. At the Games venues, owning versus hiring the DG sets was never costed. Every option worked through by hand takes time, so studies typically compare a handful of options chosen at the outset.
One future. At Rourkela Steel Plant, SAIL commissioned a ₹99.37 crore gas holder in 2010. It stopped after an incident in 2012 and was never revived, because the plant's own modernisation had changed its gas balance. CAG faulted the failure to assess the need "in the light of its upcoming Modernisation and Expansion Programme". The rules move too: Gujarat and Maharashtra have both cut solar banking from annual to monthly, which changes the economics of open-access projects planned on the old rules.
Demand and sizing assumptions that go unchallenged. At NGMA, a load fixed in 2010 was still being paid for in 2022. Chiller plants are often sized on square-feet-per-TR rules of thumb rather than on how the building will actually be used. When inputs aren't tested against a range, oversizing goes unnoticed until the plant is running.

A broader analysis was possible in most of these cases. But when each extra scenario means another round of consultant work, it is easy to see why it often doesn't happen.

Looking ahead

Where someone looked before building

Infosys: 622 TR down to 400 TR before construction

For its SDB-5 building in Mysore, Infosys worked through the cooling load measure by measure: a conventional envelope needed 622 TR, an efficient envelope 530 TR, and with the remaining design measures and high-efficiency chillers the plant came down to 400 TR. At its EC-53 building in Bengaluru, the chiller plant (chillers, pumps and cooling tower) was measured at an annual average of 0.42 kW/TR.

GIFT City: building district cooling in stages

GIFT City's district cooling is planned for 180,000 TR across three plants of 60,000 TR. The first stage, 10,000 TR, has been running since April 2015. UNEP's 2021 national district cooling study notes that GIFT City "has experienced challenges in terms of demand assessment". Building in stages is what keeps an uncertain demand forecast from turning into idle chillers.

Sources: Infosys presentation to GRIHA; REHVA Journal 03/2017 and 01/2018; UNEP, EESL et al., National District Cooling Potential Study for India (2021). Sympheny was not involved in either project.

What an optimiser does differently

Not "how does this design perform?" but "which design performs best?"

A simulation tool tells you how a design you chose performs. An optimiser searches the combinations of technologies and capacities for you, instead of evaluating only the few you thought of.

None of this replaces engineering judgement. It gives that judgement far more to work with, early, while changes are still cheap.

Not the best of the three options you had time to compare. The best of thousands.

That is what Sympheny does.

What an optimiser can't do

Good engineers with better tools

An optimiser is only as good as its inputs. At the Suurstoffi district in Rotkreuz, Switzerland, monitoring showed heating demand at twice the planning calculation, and by spring 2014 the borehole field was completely discharged. Optimising on the wrong demand would only have produced a confidently wrong answer. Spotting that a load figure doesn't fit the building type is engineering judgement, not computation.

At the Games venues, the consultant had put plug-in generators on the table, and the comparison was never made. An option backed by a full costing of variants and futures is much harder to skip than a line in a report.

That is the division of labour Sympheny is built for. The optimiser does the exhaustive part: thousands of combinations, every hour of the year, every scenario. The engineer does what no solver can: frame the question, judge the inputs, and stand behind the recommendation. Every result traces back to its inputs, so engineers can question it rather than take it on trust. And when a modelling question gets tricky, our team of PhD specialists in multi-energy optimisation, with more than ten years of research at Empa behind them, is there to back you up. In India we work with our partner ORMAE.

Building or reviewing a model yourself? Five checks before you trust an energy model →

What a better decision is worth

Can you afford to decide without it?

The cases above run to crores, and they are the ones an auditor happened to look at. A campus, an industrial site or a township carries larger numbers, and the decision is locked in for decades. Against that, the cost of an optimisation study is small. Avoiding one oversized DG installation, one chiller plant sized on a rule of thumb or one business case built on a single set of rules typically covers it many times over.

What it looks like in practice

Port of Switzerland, Basel: planning against a range, not a point

At Switzerland's only commercial port, IWB and the port's partners wanted to know what to do with a large solar surplus: export it, share it, store it, or something else. The site has many buildings and operators, very different loads, and an energy market that shifts often.

IWB used Sympheny to compare four strategies across 16 scenario variants before committing to anything. All strategies other than the status quo cut costs by up to 20–25%. The analysis also showed that regulatory change, not market price volatility, is the dominant risk, and that interlinking the buildings is the strategy that protects the port against a negative tariff change. For anyone planning open-access or captive solar in India, that finding will sound familiar.

Read the Port of Switzerland case study →

4 / 16: Strategies / scenario variants
−20 to −25%: Cost vs status quo
1: Dominant risk identified: regulation, not prices

Sources

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