This paper proposes an agent-based model that combines both spot and balancing electricity markets. From this model, we develop a multi-agent simulation to study the integration of the consumers' flexibility into the system. Our study identifies the conditions that real-time prices may lead to higher electricity costs, which in turn contradicts the usual claim that such a pricing scheme reduces cost.We show that such undesirable behavior is in fact systemic. Due to the existing structure of the wholesale market, the predicted demand that is used in the formation of the price is never realized since the flexible users will change their demand according to such established price.As the demand is never correctly predicted, the volume traded through the balancing markets increases, leading to higher overall costs. In this case, the system can sustain, and even benefit from, a small number of flexible users, but this solution can never upscale without increasing the total costs.To avoid this problem, we implement the so-called "exclusive groups". Our results illustrate the importance of rethinking the current practices so that flexibility can be successfully integrated considering scenarios with and without intermittent renewable sources. else if dif f 60(t) < −LIM IT then 43: priceH[t] ← min(price[t · 4 : t · 4 + 4]) 44: end if 45: end for 46: return priceH 47: end procedure

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In this paper, we calculate the long-term profitability of a pumped hydro energy storage (PHES) plant that is planned to be built in an old mine. We model the optimal PHES operation for several scenarios with different wind power penetration levels. Our modelling approach first involves estimating wholesale electricity prices for the day-ahead, intraday and balancing market as a function of wind power penetration. The estimated price profiles are implemented in a dynamic programming model, where the PHES plant maximises its balancing market revenue given the optimal commitment in the day-ahead market. We show that increasing the wind penetration changes the optimal PHES operation and increases the PHES profits. Additionally, we quantify how the costs of wind power balancing are affected by the PHES investment. Policy implications are drawn based on the estimated private and social benefits from the investment.

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