Recently, given the increased integration of renewables and growing uncertainty in demand, the wholesale market price has become highly volatile. Energy communities connected to the main electricity grid may be exposed to this increasing price volatility. Additionally, they may also be exposed to local network congestions, resulting in price spikes. Motivated by this problem, in this paper, we present a coordination mechanism between entities at the distribution grid to reduce price volatility. The mechanism relies on the concept of duality theory in mathematical programming through which explicit constraints can be imposed on the local electricity price. Constraining the dual variable related to price enables the quantification of the demand-side flexibility required to guarantee a certain price limit. We illustrate our approach with a case study of a congested distribution grid and an energy storage system as the source of the required demand-side flexibility. Through detailed simulations, we determine the optimal size and operation of the storage system required to constrain prices. An economic evaluation of the case study shows that the business case for providing the contracted flexibility with the storage system depends strongly on the chosen price limit.

Recently, the volatility associated with marginal prices has increased due to large scale integration of renewable generation. Price volatility is undesirable from a consumer perspective. To address this issue, we present a framework for hedging that uses duality theory for quantifying the amount of demand-side flexibility required for constraining marginal prices to the consumers maximum willingness to pay for electricity. Using our formulation, we investigate the ability of an Energy Storage System (ESS), as a demand-side flexibility source, to hedge against electricity price volatility across a multi-time period horizon while accounting for its intertemporal constraints. Additionally, we analyze the economical benefit that operating the ESS under information forecasts brings to the consumers.

Locational Marginal Price (LMP) is a dual variable associated with supply-demand matching and represents the cost of delivering power to a particular location if the load at that location increases. In recent times it become more volatile due to increased integration of renewables that are intermittent. The issue of price volatility is further heightened during periods of grid congestion. Motivated by these problems, we propose a market design where, by constraining dual variables, we determine the amount of demand-side flexibility required to limit the rise of LMP. Through our proposed approach a price requesting load can specify its maximum willingness to pay for electricity and through demand-side flexibility hedge against price volatility. For achieving this, an organizational structure for flexibility management is proposed that exhibits the coordination required between the Distribution System Operator (DSO), an aggregator and the price requesting load. To demonstrate the viability of our proposed formulation, we run an illustrative simulation under infinite and finite line capacities.

The large-scale integration of renewables to the electrical grid is resulting in the increase of price volatility in electricity markets. This increase is undesirable from both electricity producer and consumer perspectives. In this paper, we present a framework that allows consumers to hedge against the price volatility. Using optimization duality theory, we quantify the amount of demand-side flexibility that an Energy Storage System (ESS) is required to provide for constraining marginal prices to a consumer's maximum willingness to pay for electricity. The ESS is operated using Model Predictive Control (MPC) and depends on renewable generation forecasts. Forecast uncertainties are accounted through probabilistic constraints that are applied on the ESS operation. Probabilistic constraints enable the Energy Storage Operator to set a priori robustness guarantees on the solution which are cheaper than robust approaches. Through simulations it is demonstrated that the formulation is able to successfully hedge against price volatility considering uncertainty.