Optimising Battery Behaviour in Grid-Connected Energy Projects: The importance of network tariffs and the right price signals

Grid-connected energy projects are exposed to network tariffs which cover the costs of using distribution and transmission electrical infrastructure. These tariffs are complex, with costs varying based on factors such as demand, time of use, and total energy consumption. This complexity will lead to assets behaving directly as a consequence of the tariffs to mitigate cost or drive profit. If these signals are right they will likely drive the intended behaviour from the network or system operator.

Optimising battery storage systems to align with network tariffs is a critical consideration for both developers and Distribution System Operators (DSOs), especially behind-the-meter projects where these price signals are often the strongest. Based on the network tariffs that the DSOs set, developers will consider these price signals in how they operate their assets. Depending on how substantial these price signals are, the more important they are in the value stack.

Getting these price signals right is imperative from the grid and the developer’s perspective to ensuring that decentralised projects make financial sense for both parties, as well as ensuring that flexible assets behave to support rather than burn down the DSO. Today we’ll how discuss how flexible assets like batteries can be leveraged by different stakeholders to maximise their revenues.

Experimental Setup in Gridcog

To examine the impact of different optimisation strategies, I set up a project in Gridcog with the following parameters:

• Load Profile: A data centre with a relatively constant load, peaking at 1440kW and consuming 10.2GWh annually.
• Solar PV: 2000kW capacity.
• Battery Energy Storage System (BESS): 2000kWh with a 2-hour discharge duration.
• Location: London, exposed to UK Power Networks (UKPN) HV Band 4 tariff, Transmission Network Use of System (TNUoS) charges, and Day-Ahead hourly wholesale prices for exporting.

The goal was to observe the battery's State of Charge (SoC) throughout the year under three different optimisation scenarios:

1. Optimised for the Developer
   • BESS will consider the network tariffs as part of the value stack for the developer and will try to minimise the impact of network tariffs as much as possible.
2. Optimised for the DSO
   • The BESS will try to maximise the expense of the network tariff increasing revenue for the DSO at the detriment of the developer.
3. Optimised for both the Developer and the DSO
   • The impact of network tariff is largely ignored as the cashflows for each participant are in opposite directions. (The expense for the developer is the income for the DSO)

In each scenario, optimisation refers to maximising the cashflows for the respective actors.

Analysing Battery Behaviour: January Insights

The graph below illustrates the behaviour of the BESS during January. During this month, there is minimal solar export, and the site faces higher winter network charges.

One key observation is that the battery’s behaviour varies significantly depending on the optimisation strategy. For example, when optimised for the developer, the battery discharges to avoid peak network tariffs. Conversely, when optimised for the DSO, the battery charges during peak periods to capture maximum revenue.

When the battery is optimised for both the DSO and the Developer—aiming to maximise the combined Net Present Value (NPV) of both parties—the battery avoids charging or discharging during peak periods entirely. This strategy also reduces the frequency of battery cycles, which helps preserve the battery’s lifespan.

Battery Behaviour Over the Year

When the battery is optimised exclusively for the developer or the DSO, its behaviour remains relatively consistent throughout the year. However, when optimised for both actors, the BESS exhibits different behaviour from March to November compared to the winter months. During summer, the network tariffs are lower, which diminishes their impact as a price signal for optimisation. This effect is particularly noticeable during spring and autumn. However, in peak summer months, the presence of ample solar generation still prompts the BESS to activate.

Financial Implications for DSOs

The next question is: How do the network tariffs set by the DSO’s impact their own financials? To explore this, let’s consider additional scenarios involving different DSOs such as Electricity North West, National Grid Electricity Distribution, Northern Powergrid, Scottish and Southern, SP Networks, and UKPN London (the original site).

By optimising for both the DSO and the developer, the impact of the network tariffs on the BESS optimisation is largely ignored as the price signals cancel each other out. Therefore, by exploring the cashflows of the DSO under this optimisation strategy compared to when the BESS is optimised for the developer only, we can explore how much the BESS values the impact of the network tariffs on the value stack.

For instance, SP Networks is losing potentially £186,000 per site annually, just from setting tariffs that are so high that developers will avoid the peak demand tariffs to such a high degree. Considering that DSOs manage hundreds of sites, the cumulative impact on their annual cashflows could be substantial. Note how the different network tariffs for the different DSOs leads to substantially different cashflows.

Conclusion: The Power of Strategic Optimisation

Batteries are valuable assets that, when optimised strategically, can either alleviate or exacerbate grid pressures. Setting the correct network tariffs is crucial to ensure that both developers and DSOs achieve their financial targets. By setting the correct tariffs, DSOs can unlock significant financial and operational benefits that might not be possible with the wrong price signals for behind-the-meter BESS projects.

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